Environmental Information Modelling for Building Design by msc.exhibition2019

University of Westminster, College of Design, Creative and Digital Industries School of Architecture and Cities MSc Architecture and Environmental Design 2018/19 Sem 2&3 Thesis Project Module

Environmental Information Modelling for Building Design -- The visualisation of environmental strategy and parametric filter

CHIN HUEI WU September 2019

AUTHORSHIP DECLARATION FORM

ACKNOWLEDGEMENT This report brings a new sight of environmental information delivery in the construction project, as well as the overall assessment and specific simulation outcome analysis that progress by language programming. It shows the difficulty to technically apply a huge amount of specific knowledge not only based on the architecture and environmental design approach and the understanding of entire building optimisation process but also needs tool operation skills to adjust the simulation programme and visual components in the flexible platform. Therefore, I would like to thank all the professors and friends for completing the thesis project. Thanks to Dr. Rosa Schiano-Phan for all the organization of the class model and the design guideline introduction, also the course of postoccupancy evaluation and the standard of the passive house. Thanks to Dr. Zhenzhou Weng for all the support of the literature review with a useful resource, and the teaching of building form optimisation. Thanks to Mr. Kartikeya Rajput for the comment of the overall research structure and the environmental simulation framework, this collaborative project with Chapman BDSP Company is precious. Thanks to Mr. Juan Vallejo, Mr. Amedeo Scofone, and all the tutors for the comment after each presentation. Finally, I am grateful to my family for their support in my life.

ABSTRACT Environmental Information Modelling (EIM) tool development aims to solve the drawbacks of unintegrated environmental simulation tools (EST) while enhancing the communication between project members in the decision-making process at all design stages by confirming environmental strategies through the comprehensive performance evaluation. In addition, integration, filtration, and visualisation are the main development directions should be clarified by the literature review to establish the EIM theatrical basis. In the integration phase, it emphasis the adaptable selection of environmental indicators in the redefined multi-criteria optimisation framework, which should base on the brief requirement and strategic definition by project analysis. In the filtration phase, it clarifies an overall design assessment method base on the customised impact weighting system. Also, environmental consultant as the main system controller should decide an environmental target and predictable living quality by adjusting the parameters proportion and its corresponded design criteria according to building typology and climate classification. In the visualisation phase, it focuses on the EIM outcome analysis by post-processed graphical category that presents the environmental achievement comparison between all the design strategies and also simplifying the optimisation process.

Furthermore, parametric modelling, project analysis, parametric filter, and parametric visualisation are four stages of EIM tool development framework in programming which take the advantages of visual language design platform in grasshopper. To complete the EIM demonstration, this report designates four different office layouts in London as experimental objects. As the result, the EIM core value is to confirm the environmental information and potential design direction in the repeated workflow with practical strategies. In detail, it shows the parameters impact priority to define the project issue and the strategic effectiveness of specific improvement approach in the optimisation process, and then identify the strategic impact priority by the final review of best design achievement growth rate in each environmental indicator. Keywords: Environmental information modelling, multi-criteria design assessment, customised environmental indicators, visualisation of environmental strategy, informed decision making.

III

CONTENTS

AUTHORSHIP DECLARATION FORM ................. I

6.1. Basic achievement & Issue definer .......... 44

ACKNOWLEDGEMENT........................................ II

6.2. Strategic effectiveness definer ................. 45

ABSTRACT .......................................................... III

6.3. Final strategic achievement & performance ......................................................................... 51

CONTENTS ......................................................... IV

7. CONCLUSION ................................................ 55

1. INTRODUCTION ............................................... 1

7.1. Main findings ............................................ 55

Overall structure of layout .................................. 1

7.2. Reflection & Future step ........................... 58

1.1. Issue: The drawbacks of EST ..................... 2

8. REFERENCE .................................................. 59

1.2. Issue: The relationship with clients ............. 4

9. LIST OF FIGURES ......................................... 61

1.3. Purpose & Research question: Integration, Filtration, and Visualisation ................................ 6

10. LIST OF TABLES ......................................... 63 11. APPENDICES ............................................... 65

1.4. Scope & Limitation: EIM mind map & simulation programme........................................ 7 2. THEORETICAL BACKGROUND ...................... 9 Overview of EIM concept ................................... 9 2.1. Direction: Redefine multi-objective optimisation framework .................................... 10 2.2. Direction: Create environmental information filter................................................................... 13 2.3. Direction: Transfer parametric result into graphic category............................................... 14 2.4. Overall structure: EIM Framework for building design ................................................. 15 3. METHODOLOGY ............................................ 17 Overview of demonstration research ............... 17 3.1. EIM mind map and tool basis .................... 18 3.2. Case basis & Experimental objective ....... 22 4. PARAMETRIC FILTER DEVELOPMENT....... 24 Overview of the parametric filter ...................... 24 4.1. Category of benchmarks ........................... 25 4.2. Assessment method & weighting system . 31 4.3. Characteristics of parametric filter ............ 33 5. TOOL DEVELOPMENT .................................. 34 Overview of programming ................................ 34 5.1. Parametric modelling stage ...................... 35 5.2. Project analysis stage ............................... 37 5.3. Parametric filter stage ............................... 39 5.4. Parametric visualisation stage .................. 41 5.5. Final review of tool outcomes ................... 41 6. EIM DEMONSTRATION ................................. 43 Overview of EIM outcome analysis .................. 43 IV

1. INTRODUCTION Overall structure of layout There are seven chapters involved in the report to accomplish the development of the environmental information modelling (EIM) tool and take a completed tool demonstration into the final discussion.

filter, and parametric visualisation. It shows the image of potential output as the final review. The sixth chapter organises the result of EIM demonstration in a specific order. There are three analytic tasks to identify the building performance improvement layer as the simplified optimisation process, including the top issue definition in the basic achievement, the calculation of potential improvement space for each environmental indicators, and the comparison of strategic effectiveness. Furthermore, the overall output from the parametric filter will be presented in the graphic category with the visualized environmental strategies.

The first chapter introduces the background of environmental simulation tool (EST) and the relationship between users and project member during decision making. The discussion shows the drawbacks of simulation tool will not only seriously affect the smoothness of the design process and the building performance but also cause low communication because of insufficient information. Further clarification of research questions and purpose base on the issue above and the organisation of relevant simulation programme as the EIM mind map is essential to define the scope and limitation of the EIM development in this chapter.

The seventh chapter makes the conclusion that responds to research questions by identifying the EIM development process and its outcome. Also, the advantages and disadvantages of EIM, as well as the next step for further improvement in the future, will be the final focus.

The second chapter discusses three potential directions of EIM foundation, which aims to solve the issue of EST and have a significant improvement through integration, filtration, and visualisation in the design process. Creditable assumption base on the literature review includes redefining the multi-objective optimisation framework, creating the environmental indicators filter, and visualizing the environmental strategies. Finally, establishing the EIM development framework for building design as the summary. The third chapter explains the foundation for EIM demonstration includes the use of tool, the case basis of London office, the environmental simulation tasks within the thesis scope, and the experimental objective. The fourth chapter starts to create the environmental indicators filter through the literature review in details of benchmarks, as the focus of EIM development. There are two stages in the discussion, include the collection of design criteria category of environmental performance and energy respectively, and the reference of assessment method and the weighting system of indicators. Finally, the characteristics of the parametric filter are described based on the complete setup. The fifth chapter practices the real EIM tool development on the selected platform, which refers to the programme of rhino and grasshopper. To complete the EIM framework in four stages include parametric modelling, project analysis, parametric 1

1.1. Issue: The drawbacks of EST Environmental simulation tool (EST) as a common approach for evaluating the building performance and providing predictable result is quite convenient for the designer to improve the result during the repeated optimisation process. Also, the output from building performance simulation (BPS) is essential for defining the environmental issue in the early stage that can consistently correct the design direction to achieve a sustainable target. However, the EST information output still insufficient to apply in the real project. Although the BPS result can be technically confirmed for each environmental indicators respectively, it is still difficult to confirm the appropriate strategy with project member, especially to the client who did not have a professional background during the decision making, and further impact the final performance. According to the relevant research that provided the category of EST nowadays, such as Grasshopper, Energy Plus and Autodesk CFD, each of them can be used to conduct the BPS with a specific environmental indicators include energy, thermal comfort, daylight, or computational fluid dynamics simulation respectively (Naboni, E., et, al, 2013). Nevertheless, this category also shows complicated simulation tasks in the overall design process, which can cause the difficulty of familiarizing plenty of tools at the same time and collecting data from separated platforms in different formats. It should be noticed that the operational complexity of EST will be risky of misleading the environmental design with inappropriate strategies. Furthermore, most of the report only shows the BPS framework description without realisation. Or, it only focuses on one specific issue in few selected environmental indicators instead of comprehensive study. For example, the building form optimisation with final energy performance evaluation did not take any other factors of indoor living quality into consideration. Which means the lack of integration of analytic work with essential parameters will be the challenge in the future, as well as the credible transformation from BPS output to practical environmental strategies. The latest report from Aksamija can be a potential solution to avoid separated BPS generation and overlapping calculation while eliminating the confusion from project member during the design process. It provides an ideal workflow of integrated parametric design for three specific tools, Rhino 3D, Revit, and Sketch Up, which can deal with energy modelling, solar radiation, and daylight simulations, and finally import the comparable result of energy use intensity (EUI) through the Energy Plus (Aksamija, A. 2018).

Figure 1.1.1. Table of EST today used in the analyzed architectural practices (Source: Naboni, E., et, al, 2013)

Figure 1.1.2. Ideal framework for parametric and performance-based design (Aksamija, A. 2018)

In details, it shows significant progress in the integrated approach that offers a comparable result by transferring all the BPS output and environmental performance into the calculation of energy use intensity (EUI). Although the assessment method is convenient, EUI as a common unit still involved two concerns. On the one side, how to perform the simulation with the environmental indicators which are not transferable into the energy consumption figure but more related to the indoor living quality enhancement? On the other side, how to conduct the visualisation of environmental impact with relevant architectural design factor rather than only display plenty of numerical results, also enhance the clients understanding?

Secondly, the BPS output is non-transferable from the 2D graph into actual environmental strategies, the wrong interpretation in between will be an issue during the design stage. Thirdly, the insufficient strategies did not reflect the project requirement because of the low communication between project members, and loss the consideration of occupant behaviours and indoor quality. It evident that the closed circulation in the design process, which only confirmed by the professions without clients, will result in the negative influence and misunderstand in all stages. Therefore, the development of environmental information modelling (EIM) as a new concept of BPS optimisation, especially the integrated information platform will be the focus of this report.

In summary, there are three drawbacks of EST should be improved with new concept:

To further clarify the definition of EIM, further literature review which related to the applicability of building optimisation framework with information modelling, selection of essential environmental indicators, and the confirmation of environmental strategies will be discussed under the hypothesis.

First of all, unintegrated EST causes separated simulation result and overlapping calculation from repeated workflows.

Figure 1.1.3. Drawbacks of environmental simulation tool (Source: Personal data, 2019)

1.2. Issue: The relationship with clients Lack of communication can be an obstruction to achieve the sustainable target of the built environment, which is an important issue that has always been ignored in construction projects. Usually, the expectation from each project member will be different in several aspects, such as the request of aesthetics, the reduction of construction cost, the rent price, or the indoor living quality. To maximise the benefit, the discussion between designer and client group should reach a balance consistently, and define a realistic target base on the overall BPS assessment collaboratively. From the category derived from RIBA 2013 (Robinson, E., et, al, 2016), it shows the reasonable composition of project members who should engage in the meeting at each design stages. It evident that client and architects are in a large proportion to influence the final decision making and further impact the building performance in the future steps. In addition, financer as a responsible housing provider should keep in touch with professionals and take the environmental issue into consideration, while the designer should follow the recommendations of the EST controller to implement potential environmental strategies. However, this ideal teamwork model still has not been generalized in recent year. For example, unilateral decision making with stakeholder and financer shows less consideration of the indoor living quality and satisfaction from the client and end-user, which means the inadequate study of project requirement will impact the final BPS in a vicious cycle and result in unwanted energy loads. To define the problems in the relationship, Norouzi summarised the characteristics of the design approach that can enhance communication, including three factors in different aspects, namely tools, message, and relationship. (Norouzi, N., et, al, 2015).

Figure 1.2.1. Relationship between designer and clients (Source: Personal data, 2019)

Using tools as an example, if EST becomes the media that can integrate the client information into the strategic definition and further provide understandable BPS outputs and corresponding design directions in response to project requirements, the final result will benefit from positive communication with sufficient information.

In summary, the mechanism of EIM project has been clarified, which shows the difference from EST. Firstly, EIM can help environmental consultant to integrated the environmental information and improve the connection between groups. Secondly, EIM can generate environmental strategies which fit into the occupant behaviours by following the strategic definition from clients and result in better living quality as well as energy performance.

The possibility also indicates the research direction of EIM development. Base on the previous discussion of simulation tools, it is obvious that the BPS output can only be interpreted by the environmental consultant or professionals rather than other project members, not to mention the client, even architects cannot replace the role of them without specific knowledge. If there is no information sharing system between the designer and the customer base in the workflow, then the serious impact on the final building performance can be inferred immediately.

Thirdly, EIM can visualize the BPS output practically, which is understandable for both designer and client to confirm the effectiveness of design for the decision making. As a potential solution, EIM has a positive impact on clients and local communities through better communication during the design process. Before the technical operation, the concept and objectives of EIM will be more clearly based on the above statement, which will also help to clarify the research questions in the next chapter.

It states that the integration approach should be considered properly to eliminate the deviation between the expectations of each group for decision making. Therefore, creating a common and understandable language to enhance the communication in the design process while confirming the progress direction with appropriate strategies is extremely important for the building performance optimisation in real practice. Also, the information delivery with technical assistance will be the focus for the EIM development.

Figure 1.2.2. Relationship between designer and clients (Source: Personal data, 2019)

1.3. Purpose & Research question: Integration, Filtration, and Visualisation From the previous discussion, three research questions of EIM development have been defined.

Moreover, this thesis aims to complete the EIM system that can solve the drawbacks of simulation tools while clarifying the relationship between designer and client. In particular, the environmental consultant as the main controller of EIM should always lead the project in the right way to upgrade the effectiveness of collaboration as well as the environmental performance.

Firstly, environmental information should be fully integrated into the same format on a continuous platform that can ensure the overall comparison between each indicator by improving the original function of BPS and the component from the EST. Secondly, strategic definition and project requirement should be practiced in the design process by fliting out the needless environmental indicators and BPS base on the early discussion with the client.

In summary, EIM development has three research directions, including integration, filtration, and visualisation. Further literature review such as building performance optimisation framework, integrated information approach, relevant benchmarks, and the possibility of visualisation will be the focus to create the initial hypothesis as EIM foundation.

Thirdly, the BPS output should be simplified from the specific pattern to the clear display for clients, which can be regarded as the visualisation of environmental strategies. Therefore, generating sufficient results to enhance communication in the decision-making process at each design stage, while solving the above research questions will be the purpose of EIM development.

Figure 1.3. . Research question: Integration, Filtration, Visualisation (Source: Personal data, 2019)

1.4. Scope & Limitation: EIM mind map & simulation programme In the line of purpose, it should focus on the full demonstration with completed EIM system rather than result in the perfect optimisation or powerful strategies to be the final outcome. In addition, EIM is considered an upgrade tool for various BPS and environmental programmes in different countries under local climatic conditions. However, it is impossible to show the EIM applicability on a worldwide scale in limited pages. Therefore, the research scope and potential outcomes of EIM development should be determined as early as possible before starting the work so that the project can be completed within a limited timeframe.

Firstly, the collection of relevant environmental performance benchmarks and local design criteria based on a building typology and the climate condition, such as the office in London. Secondly, the selection of essential environmental indicators to conduct the BPS effectively to achieve the target from benchmarks research. Thirdly, the list of potential architectural design factors as environmental strategies that can be recorded in the graphic category, including building orientation, window and wall ratio, materiality and construction layers. It can correspond to integration, filtration, and visualisation stage that will be discussed more in the methodology chapter.

The mind mapping of environmental simulation programmes will be a good point to organise the idea for EIM demonstration and create a research foundation. The following figure shows an example of plenty of environmental stimulation and analysis tasks. It also includes weather file preparation, environmental strategy conversion, and architectural design parameters. All the programme above which base on the overall function from EST will be the database of EIM that can be operated in the continuous platform while enhancing the applicability.

In summary, the thesis scope except for the basis of weather tasks and BPS analysis will depend on the following assumption: ● ● ● ● ●

Building typology. Benchmark selection. Environmental parameters selection. Energy performance parameters selection. Environmental strategies and architectural design parameters selection for the graphic category.

Also, the thesis limitation as follows:

However, it should be noticed that a clear project basis and brief requirements are necessary to customise an appropriate research programme while defining the target for the completed EIM demonstration. Therefore, there are three key foundations should involve in the EIM mind map.

● Upgrade of a specific EST with programming. ● Focus on completed tool development rather than the progress of strategies. ● EIM tool demonstration cannot include all the simulation tasks.

Figure 1.4. Thesis scope and selected target (Source: Personal data, 2019)

2. THEORETICAL BACKGROUND Overview of EIM concept Based on the previous introduction of EIM as an upgrade EST to enhance the cooperation in the design process, it is obvious the further research of the latest EST and BPS approach should be the first step for the EIM establishment. Moreover, EST relevant reports should be organised into three improvement directions that respond to integration, filtration, and visualisation in purpose. In details, one of the requirements for the EIM tool is to clarify the optimisation framework through a comprehensive environmental assessment and multidimensional integration mechanism. Another request is to enhance the assessment method of BPS result by taking all selected parameters and environmental indicators into the calculation. Last but not least is to visualise the assessment outcomes and the achievement comparison in a simple way. Therefore, this chapter will have a confirmation of three hypotheses through the literature review: ● ●

●

Firstly, redefining the integrated information programme based on the multi-objective optimisation framework. Secondly, creating the environmental information filter to evaluate essential parameters by following the project requirement and relevant benchmarks. Thirdly, visualizing the environmental strategies in a practical way which is easier to understand.

In addition, the characteristics of the EIM framework will be pointed out after all the discussion, such as the flexibility of changing parameter selection on a case-by-case basis. Finally, the conclusion will be three workflows of EIM tool development. It can be implemented as a guideline in the following chapters, including parametric filter development through the literature review, EIM development base on the reprogramming selected EST, and the final EIM demonstration by translating indicators into the graphic output.

2.1. Direction: Redefine multi-objective optimisation framework The latest trends of EST application focus on building performance optimisation and specific improvement subjects such as the reduction of energy consumption as well as high thermal comfort.

Firstly, building performance simulation (BPS) is a useful approach to define the environmental issue in the early design stage by EST user. Also, experts can not only update the design proposal but also figure out the potential strategy base on the repeated simulation tasks. Therefore, the iterative operation of BPS that aims to reach a higher level of building performance can be called simulationbased optimisation. (Konis, K., et, al, 2016)

However, the regular EST case study only shows a unilateral environmental strategy with a changeless target. For example, the building form optimisation research focuses only on the adjustment of building geometry and orientation as the primary input controller. Besides, BPS achievements include only a few environmental indicators that do not have selection criteria, rather than an overall assessment base on the clear project requirement.

Second, Performance-based design (PBD) as a design guideline (Ekici, B., 2019) aims to consistently confirm the building performance evaluation during the design process and take advantage of immediate feedback and immediate adjustment, such as geometry formation. PBD executor can be architects or designer with environmental acknowledge and experience using EST, not just environmental consultants.

Although EST reports have provided a strong BPS foundation and specific solutions for each environmental issue in recent years, it should be clarified that the integrated environmental information of building design remains the challenge of using EST. Therefore, the future trends of EST in the environmental construction discipline will be the information integrated approach and technical communication between project members, which means a brand new concept of EIM.

Taking the Pourel report as an example, it aims to improve the indoor quality of residential homes in the UK, which can be achieved through environmental assessments at every design stage from a broader climate perspective to room details. (Pourel, D., 2017) The structure of PBD has been proven in the end.

Back to the literature review, some topics cannot be excluded from the EST operation. According to the statistics, the number of studies related to building envelope optimisation is the largest proportion in the field, which is 58%. Besides, the building shape and layout optimisation is 23% and 19% respectively (Ekici, B., 2019).

Third, multi-objective optimisation (MOO) as the latest concept points out the importance of integrated multi-objective information in a broader simulation programme, which aims to consider all possible parameters with the relevant standards and variable building elements, and further upgraded the design procedure.

In summary, building form optimisation is one of the popular research topics in the past few years. Taking advantage of EST, specialist consistently studying how to automatically generate an efficient building geometry that can achieve the target of final building performance, which mainly referred to energy use intensity (EUI).

It should be noticed that the first two terms also mention the possibility of MOO. However, due to database overload and concerns about unwanted environmental indicators, they all indicate that it is difficult to achieve MOO in both theoretical and practical way.

However, in the integration point of view, we should figure out the possibility of programming and the basis of selecting parameters from the simulation frameworks, rather than emphases on the effectiveness of optimisation approach.

Finally, performative computational architecture (PCA) as a completed design framework includes not only the idea of repeated simulation tasks in the process but also the comprehensive project evaluation in four different directions, which is sustainability, cost, functionality, and structure aspect. (Ekici, B., 2019) From the broader PCA taxonomy, it evident that improving the lack of balance between each parameter through the effective weighting system is a challenge in the future.

The following definition of key terminology in the optimisation process aims to make the structure clearer. Four approaches will be discussed separately to distinguish their target and instruction in the design process, including building performance simulation (BPS), Performance-based design (PBD), Multiobjective optimisation (MOO), and Performative computational architecture (PCA).

Table 2.1.1. Literature review: relevant terms of optimisation (Source: Personal data, 2019) Function Stage Terms Approach Before/ after Building performance simulation design (BPS) Guideline

During design

Approach

During design

Framework

Iterative design process

Performance-based design (PBD) Multi-objective optimisation (MOO) Performative computational architecture (PCA)

Basis of selecting parameters Multi-objective of daylight, energy and thermal comfort in specific case

Reference Konis, K., et, al, 2016

Multi-criteria in specific case without adaptable method Multi-objective without details

Pourel, D., 2017

Multi-criteria without details

Overall speaking, researchers started to emphasise on the multidimensional BPS assessment in the design process.

The advantage of integration is not only to get a larger database from the undifferentiated collection. It should emphasize the effective simplification of various input factors while classifying environmental indicators in order of priority to meet project requirements.

However, it still has a question based on operational settings. Design approach without the consideration of project details such as climate data and client requirement will be hard to practice in the real construction case, even though how comprehensive it is in the theoretical discussion. On the other hand, no reports have mentioned about the logic of selecting environmental indicators and relevant benchmarks in the general case. Which means the design optimisation demonstration usually shows the improvement of single parameters such as daylight, energy, and thermal comfort intentionally rather than provides an adaptable framework with overall BPS comparison.

Furthermore, the connection between different groups of information should be clarified through integration. For example, standard applications are related to the design range of the selected parameters individually, which should be considered in the evaluation platform by following the decision making. There are two reports in the past two years shows the integration approach in a different aspect. The first report from Berechikidze applies spatial mapping to visualise the overlapping result of indoor thermal comfort. It shows the comparison between each scenario is clear from an integrated image.

Therefore, selecting parameters and benchmarks based on project brief assumptions is critical to logically validating environmental information integration. Also, it will create an adaptable framework that can be generalized around the world to eliminate concerns about lost realities and impractical self-definition.

However, the limitation of spatial mapping is obvious. This approach only applicable when there is only a single environmental indicator as to the optimisation target in the research. Otherwise, the final figure will be messy to display due to different kinds of legend bar and colour scheme from other indicators in the combination.

Based on the limitation of MOO, the following discussion will focus on the possibility of the integrated approach that can reorganise the analytic task as well as enhancing the optimisation process.

Table 2.1.2. Literature review: relevant terms of optimisation (Source: Personal data, 2019) Function Target Terms Approach

Comfort mapping

Spatial mapping

Approach

Factor mapping

Monte Carlo Filtering (MCF)

Touloupakia, E., et, al, 2017 Ekici, B., 2019

The second report from Ø stergård also has a general goal of the balance between thermal comfort and energy performance. Impressively, it pointed out a specific stage called “explore design space” (Østergård, T., et, al, 2017) during the meeting with all stakeholders through the description of the iterative design framework.

Basis of selecting parameters

Reference

Multi-criteria of thermal comfort and energy consumption in specific case Multi dimensional design space from variable filter criteria

Berechikidze, S., 2018 Ø stergård, T., et, al, 2017

Figure 2.1.1. The iterative framework. (Source: Ø stergård, T., et, al, 2017)

As a new idea of informed-decision making, it can help to define the project target and design options by filtering the necessary BPS inputs and outputs. Furthermore, it shows the advantages of comprehensive BPS outputs analysis by following the strategic definition and selected criteria. At the end of the research, the multidimensional design space as the key result of the overlapping analysis proves the possibility of overall indicators comparison. Therefore, Ø stergård shows the core value of the integration and the advantages of the information filter in the early discussion. In conclusion, the new definition of multi-criteria design space which can include all the indicators and benchmarks in the adaptable selection will be the focus of EIM development, rather than the research of multi-objective optimisation with a changeless environmental target. To have an upgrade integration approach in the EIM, completing the hypothesis of creating an environmental information filter will be a good start in the next chapter.

Figure 2.1.2. The design space in multi-dimension. (Source: Ø stergård, T., et, al, 2017)

Figure 2.1.3. Redefine multi-objective optimisation framework (Source: Personal data, 2019)

2.2. Direction: Create environmental information filter The environmental information filter is the most important concept of EIM, highlighting the adaptability of environmental indicators at any design stage.

Secondly, the specialist has to complete two tasks for the filter establishment, including the manual collection of relevant environmental design and energy benchmarks for each selected indicators base on the building typology, local climate, and brief requirement, also the additional concern from client and designer group. Afterward, conduct the technical calculation of customised indicators weighting system through the criteria setting in EST.

There are three benefits of filtration in the integrated process. Firstly, it improves better communication with clients through the decision making of input data while customised the project target that respond to environmental requirements in a practical way. Secondly, it provides the integrated platform that can transfer all the BPS setting into the comparable format. Finally, as the comparison outcome, the multi-criteria design space can clearly define the overall project issue and the effectiveness of strategies that can improve the decision making between project members. It should be noticed that all the adjustment above is based on the professional knowledge to ensure validity and reliability.

Thirdly, various factors in the BPS workflow should separate into three parts. The benchmark collection base on the environmental condition is the foundation of BPS. Besides, the architectural design strategies such as the adjustment of geometry will be the input. In the end, the BPS results will be translated to a communicated parameter as the output to confirm the strategic priority and effectiveness. Finally, environmental information filter is regarded as a parametric filter in the operation of EST, which refers to the programming aspect. Base on the above statement, the EIM development stage can be assumed to be four parts, including parametric modelling, project analysis, parametric filter, and parametric visualisation. The details of each development framework will be clarified in the last section of this chapter.

Furthermore, the following four statements can further clarify the phase of creating the filter. First of all, the environmental information filter as the language translator between project members will share the simulation outcome also with the input details in the integrated platform to ensure a correct decision making in the design process. In addition, environmental consultant as the professionals will be the primary filter controller to clarify the environmental design direction consistently.

Figure 2.2. Create environmental information filter (Source: Personal data, 2019)

2.3. Direction: Transfer parametric result into graphic category As the previous discussion in the introduction chapter, the relationship between specialist, designer, and clients is the key to influence the final building performance. Also, the latest research of the integration approach shows the benefit of considering both side concerns and the possibility of implementation through the environmental information filter.

Secondly, the numerical BPS report should be presented with the corresponding design strategies such as the type of geometry or other architectural information in a graphic display. In this way, each environmental solution and their achievements will be fully understood by the other project member. Thirdly, the priority of each BPS result in the graphic category should respond to the design process carefully. Which means the operational complexity from repeated simulation works should be simplified by following the schedule of design testing and commissioning.

However, the parametric filter outputs still incomplete to present to other project members. On other words, there is plenty of simulation output included the impact of each environmental indicators with the multiplication of the numbers of test scenarios, also the final strategy performance and environmental achievements.

As a result, the graphic category should include the simulation output from the basic construction and all the design strategy while informing the final score of environmental quality as communicated parameters.

Therefore, there are three tasks at the stage after the application of the information filter, which aims to have a sufficient result from parametric visualisation.

In short, it focus on not only the organisation of output but also the simplification of optimisation process. Parametric visualisation aims to convert the parametric output into the graphic category while simplified the comparison between initial building performance and the environmental quality achievement for each practical strategy.

Firstly, well organisation through the postprocessing analysis of the filter outputs is essential to ensure the progress from each environmental strategy. It shows the difference between the building information modelling (BIM), which only consider the BPS as an intermediate process that is not necessary to share with clients.

Figure 2.3. Transfer parametric result into graphic category (Source: Personal data, 2019)

2.4. Overall structure: EIM Framework for building design At the end of the chapter, three assumptions about the EIM foundation have been prepared to predict the overall structure and potential outcome.

As the guideline, there are three workflows involved in the EIM tool development structure that respond to the hypothesis above.

Firstly, the integration phase of EIM aims to ensure the adaptable selection of environmental indicators and the benchmarks collection, then output a multicriteria design space as the project target in the programme.

The framework of parametric filter development should be the focus in the beginning to ensure the case basis and benchmark resource through the methodology and literature review. In details, there are two tasks should be completed, including the manual collection and the technical calculation for programming.

Secondly, the filtration phase of EIM aims to settle up the customised environmental indicator weighing system into the EST platform, which means an environmental assessment method base on the integrated information of selected benchmarks. Afterward, the outcome will be the customised score of each design strategy and its overall environmental impact in details.

Moreover, the framework of EIM tool development will base on editing the parametric filter into the EST programme and selected BPS settings and clarify the overall workflow of the customised assessment method, which means the improvement from EST.

Thirdly, the visualisation phase of EIM aims to conduct the post-processing analysis of numerical results and design strategies modelling while simplifying the optimisation process into the graphic category.

The last but not least, the framework of EIM chart development which also related to the parametric visualisation process will base on recording the full EIM demonstration from the case basis to the final achievement of each design strategy and their impact priority.

Therefore, the EIM structure is different from the tradition EST application and the BPS objectives. It shows the progress of the parametric filter and parametric visualisation after the preparation of the test model and BPS templates.

Further description of each framework will be presented in the corresponding chapter. In addition, the practicality of EIM will be confirmed simultaneously when all development frameworks are completed.

Figure 2.4. EIM Framework for building design (Source: Personal data, 2019)

3. METHODOLOGY Overview of demonstration research Demonstration research can be the appropriate methodology to prove the core value of EIM framework, also ensure the potential benefits and practicality of the system.

In summary, this chapter will discuss the effectiveness of the demonstration, with the following key points: ● ● ● ●

There are several considerations of EIM demonstration, including environmental information input preparation, case basis assumption, tool development guideline, tool operation procedure, case achievements, and outcomes. Therefore, demonstration research that practiced with scientific computing will be sufficient to control plenty of variables in the simulation process while promote the new EIM concept by following the thesis scope and limitation. Also, further discussion is needed to clarify the demonstration design programme, which should base on the corresponded research direction, including integration, filtration, and visualisation. The following four paragraphs demonstration preparation.

are

for

the

Firstly, the quality of simulation tasks and mathematic calculation depends on the function of selected EST, which will become the prototype of the system while providing initial workflows from the database. Secondly, the mind mapping of the environmental simulation programme is essential to determine the limited operation scope in the report and respond to the integration approach. In other words, the case basis as the experimental subject should be provided in the beginning to ensure the environmental condition, sufficient building information, and finally clarify the integrated simulation task. Thirdly, the benchmark settings for each selected environmental indicators in the simulation programme should also follow the case basis and respond to the filtration approach. The stage should focus on the possibility of new programming, such as the performance assessment range translation and the customised score calculation. Fourthly, experimental objectives should be clarified base on the outcomes such as final building performance and environmental strategies achievement, and then respond to the visualisation approach. In anticipation, the 3D modelling with visualised component is the key to output the building geometry and spatial configuration into the graphic category, which still need the postprocessing analysis.

Tool basis. EIM procedures. Case assumption. Experimental objectives.

3.1. EIM mind map and tool basis To provide sufficient technical support based on the effectiveness of a particular EST that is important to establishing the entire simulation of the EIM. In short, the designated EST should have three characteristics, including the adaptability of the continuous simulation platform that can conduct most kinds of environmental evaluation. Moreover, the adjustable component for a new programme establishment not only can be used to review the operational issue but also upgraded the function of EIM. The last but not least is the capability of modelling and graphic visualised component that can be used to output the image to simplify the environmental information.

Table 3.1.1. EST basis & component: Rhino and Grasshopper (Source: Websites, https://www.ladybug.tools/, 2019) Ladybug Honeybee Butterfly Daylight/ Energy Weather Data Simulation Import Airflow Modelling Energy, Daylight, Graphic Analysis Comfort modelling

Based on the above description, Rhino and Grasshopper as the architectural design platform with the visual programming language (VPL) and environment can become a powerful EST to contribute to the EIM development with relevant simulation plugin and variable components, and also meet the automatic calculation requirement. The following table shows the features of selected components respectively, including the Ladybug which aims to conduct the environmental and climate analysis tasks by input the EPW weather file, also the Honeybee and Butterfly are mainly supporting the BPS such as annual daylight, thermal comfort, and ventilation analysis by using relevant plugin like Day Sim, Energy Plus, and Open Studio. In addition, once the EIM system and its inputs and outputs are properly defined, components such as Octopus and Galapagos that designed to enhance multi-objective optimisation and facilitate repetitive processes will generate all possible outcome effectively.

EPW File

Day Sim

Energy Plus Weather Data Format

1. Annual Daylight Analysis

Adaptive Comfort Parameters

Radiance

Open Studio

US (ASHRAE) or European (EN) standards

1. Annual Daylight Analysis 2.Daylight Analysis 3.Glare 4.Electric lighting (Lighting Analysis, Visualisation)

Cross Platform Software Tools Whole Building Energy Modelling

Octopus Octopus is a plugin for applying evolutionary principles to parametric design and problem solving.

Figure 3.1. EST basis: Rhino and Grasshopper (Source: Personal data, 2019)

Energy Plus 1.Outdoor Comfort 2.Indoor Comfort 3.Energy Modelling 4.HVAC Sizing (Heating, Cooling, Lighting, Ventilation Simulation, Energy Flows, Water Use)

Open FOAM 1. Outdoor Airflow 2. Indoor Airflow 3.Buoyancy 4.Outdoor Comfort 5.Indoor Comfort 6.HVAC System

Therm/ Window Thermal Material and Boundary Condition U-Value Calculator

However, the capability of simulation components is not the only focus on the programming basics. When it comes to confirming the EIM development framework by applying grasshopper appropriately at each design stage, the relationship between tool functions, analysis tasks, and environmental parameters should also be well defined in the EIM mind map.

Furthermore, the next section nearby shows all the simulation task, analysis task, environmental strategies application and architectural design strategies application in a workflow to confirm the transformation of each series factors and their meanings. As an extension review in the process, except the weather analysis will be conducted in the early stage to define the environmental issue, the other simulation programme are started from the filtration process that aims to filter out any unnecessary tasks base on the project requirement.

There are two sections in the EIM mind map. First of all, the operational procedure with EST basis should be discussed in details while responding to the research direction. The first stage of parametric modelling includes geometry and microclimate analysis preparation which means to input the test model information such as building form orientation, materiality, construction layers through Grasshopper programming or Rhino modelling, as well as the weather file, analysis period, and surrounding context by using ladybug plugin.

The first list of BPS analysis tasks includes lighting, thermal comfort, and energy analysis right now. However, the list is adjustable if there are any other issues or client concerns should be considered and join the overall assessment in the customised impact weighting system, such as acoustic or air pollution analysis. The second list of PBD factors is based on the BPS result to provide sufficient environmental strategies in the scheme design stage, which means to improve the performance by following the environmental design principle, such as the cross ventilation for natural cooling or thermal mass for indoor temperature.

The second stage of project analysis includes brief requirement and strategic definition which should respond to the integration research. Simply speaking, the project details such as building typology, microclimate performance, internal condition and daily tasks just like typing keywords on the search engine, and the search outcomes will be the relevant environmental design and energy criteria also the selection of the environmental indicators. In addition. the professionalism of environmental consultants are playing the role of the search engine and have the responsibility to set up the benchmarks in the system through language programming components.

However, the theoretical effectiveness of strategy is difficult to be applied directly. Therefore, the third list of architecture design parameters is a significant language transformation to ensure the variables as the appropriate controller which means to optimise the performance by adjusting the input of test model. For example, the optimum orientation and aspect ratio will contribute to the lighting performance, while the optimum U value of envelop can result in a good thermal performance.

The third stage of creating a parametric filter includes all the designated environmental indicators and their performance simulation template into the customised impact weighting system, which should respond to the filtration process. Base on the benchmark settings from the previous step, the environmental assessment will be fully completed by transferring the original simulation output into the evaluable range then output the final score through the automatic calculation of new programme. Therefore, all the BPS analysis by using ladybug, honeybee, and butterfly are still important to generate the initial result.

The final list of communicated parameters inside the graphic category are the integrated design strategy information, which shows five types of solution, including geometry dimension, building element, materiality, and construction layers. In this way, the architectural design factors become the variables and result in the final achievement comparison that will be powerful to figure out the effectiveness of each strategy. In summary, the EIM mind map shows the completed workflow with the proper programme while ensuring the strategic interpretation between different series of analysis tasks that will help to promote the EIM development.

The final stage of parametric visualisation includes the communicated parameters and graphic category as outcomes, which should respond to the visualisation goal. After the post-processing analysis and the comparison with the full score of customised impact weighting system which generated by the parametric filter, the progress of building performance, environmental achievement, strategic effectiveness, and strategic impact priority will be clarified. 19

Table 3.1.2. EIM Mind map of development steps and tool use (Source: Personal data, 2019)

EIM MIND MAP &DEVELOPMENT Rhino Modeling

EIM research direction

Test model

Firefly

EST BASIS: RHINO & GRASSHOPPER COMPONENT (DIVERSE GEO VS SIMPLE GEO) Parametric Diva Ladybug Honeybee Filter

Butterfly

Octopus/ Galapagos

EIM tool development

Parametric modelling

1. Geometry preparation 1.1. Orientation

Modelling

Simple model basis

1.2. Geometry dimension 1.3. Building element 1.4. Material properities

Therm/ Window

1.5. Constructionlayers 2. Analysis preparation

Weather data collection

2.1. Surrounding context

Epw File

2.2. Weather file 2.3. Analysis period 2.4. Sky selection Integration

Project analysis

3. Brief requirement

3.1. Building typology

Programming

3.2. Microclimate 3.3. Internal condition 3.4. Schedule 3.5. Dayliy task 4. Strategies definition

4.1. Benchmarks selection 4.2. Indicators selection 4.3. Simulation template selection

Filtration

Parametric filter

5. Environmental dsign assessment

5.1. Customised impact weighting system UTCI (Level) VSC (%) SCF (%)

Epw File

Day Sim

Complex model basis

Octopus /Galapagos

ADF (%) UDI (%) Daylight illuminance (lux) ASPH (Annual) (h) ASPH (Summer) (h) Radiance

Average Radiation (Wh/m2) Horizontal Raidiation (Wh/m2) Façade Radiation (Wh/m2)

OpenFOAM

Outdoor CFD (m/s) Indoor CFD (m/s) Air Quality (ppm) Open studio/ Energy

Thermal mass (°C) Internal heat gains and loss (°C) Acoustic Reverberation Rime (s) 6. Energy performance assessment

Annual Resultant Temperature (°C) Open studio/ Energy Plus

Seasonal Resultant Temperature (°C) Annual Energy Loads (kWh/m²·year) Heating Loads (kWh/m²·year) Cooling Loads (kWh/m²·year) Frequency of occupant hours (%) Energy Use Intensity (kWh/m2) 6.1. Customised score calculation

Programming

6.2. Overall review Visualisation

Parametric visualisation

7. Communicated parameters

Therm/ Window

7.1. Building performance 7.2. Environmental achievement

8. Graphic category

8.1. Strategic effectiveness 8.2. Strategic impact priority

Notice: * Gray column means the potential of component, however, it will not be used in the report. * Bright yellow column means the programming of parametric filter. * Pink column means Ladybug plugin. * Yellow column means Honeybee plugin. * Blue column means Butterfly plugin.

Table 3.1.3. EIM Mind map of simulation tasks and design factors (Source: Personal data, 2019)

SIMULATION PROGRAMME

ENVIRONMENTAL STRATEGY

DESIGN STRATEGY

GRAPHIC CATEGORY

BPS Analysis Task

PBD Programming stage

Architectural Design Darameters

Communicated parameters

Climate analysis Base case analysis Parametric analysis

PBD Customising stage

Weather analysis

Environmental assessment method

Sunpath Analysis

Building typology

Customised impact weighting system

Wind Rose analysis

Microclimate

Customised score calculation

Humidity and Arecipitation analysis

Benchmarks

Final performance over review

Psychometric analysis Min/Max temp range PBD Schmetic design stage

Diurnal temperature variation Right to light analysis

Orientation

Geometry Dimension

Optimum orientation

Total Gross Floor Area (m2)

Messing

Number of floors

Aspect ratio, volume

Number of occupants/working places

Zoning

Height

Lighting analysis

Envelope

Length

Daylight factor

Insulation

Optimum U, R values, thickness

Depth

Illuminance level

envelope - opaque

U, R values, thickness

Window/ Floor Ratio

Daylight autonomy

envelope - glazed

SHGC, VT, U value

Window/ Wall Ratio

Glare index

Green/ Cool roof

Optimum WWR

Window Type

Thermal analysis

Lighting

U,R values, thickness

Shade Type

HDD/CDD

Daylighting

Lighting

(Size/ Level/ Efficient Aperature)

Incident solar radiation (Insolation)

Light shelves

Optimum DF, WWR, SHGC, VT

Building Element

Air change rate

Daylight zoning

Location, DF

Building Envelope

Infiltration gain

Skylights

Optimum DF, SHGC, VT

Celling

Room Temperature

Sensors controls

Sensor location

Floor

Heat storage/removal capacity

Cooling/ Vent

External wall

Occupancy gains

Cross ventilation

Window opening area, location

Internal wall

Conduction gain

Stack ventilation

Stack height, location, opening area

Window

Direct Solar gain

Mass+night cooling

Area of thermal mass/ openings

Shade

Lighting gain

Shading

Geometry, location

Furniture

Timelag in heat transfer

Evaporative cooling

Material Properities

Overall heat gain & loss

Earth cooling tubes

U Value

Heating load

Absorption chillers

G Value

Cooling load

Heating

Density

Reduction in heating & cooling load

Thermal mass

Area, location, thickness, heat storage capacity

Conductivity

Energy analysis

Direct solar gain

WWR, SHGC, location

Resistance

Base case end use energy breakdown

Indirect heat gain

Thickness

Absorption

Proposed end-use energy breakdown

Heat recovery

Heat storage capacity, location

Transmittance

Heating energy

Energy

Reflectance

Cooling energy

Solar power

Panel sizing

Coefficient

Savings in lighting energy

Wind power

1.5. Construction Layers

Reduction in heating energy

Solar DHW

Thickness

Reduction in cooling energy

Heat pumps

External Surface

Overall energy consumption

CHP

Internal Surface

Shading mask Cloud cover analysis

Messing

Overshadowing analysis

Energy generated Notice: * Gray column means the overall simulation tasks which can be applied in the EIM. * Yellow column means the organization of design factors and variables for the EIM outcome.

3.2. Case basis & Experimental objective From the previous description of EIM mind map that connected to the development workflow, it shows the project analysis started in the early stage is essential to integrate the building reference, case study, and relevant regulation, then further create the foundation for the parametric filter.

In summary, based on the different case setting, the experimental objective is not only to examine the final achievement separately but also to determine the meaning of final score generated from the parametric filter, and then identify potential improvement strategies for each case.

The following table shows the full case-based assumptions to complete an EIM demonstration with a clear target. Overall speaking, office as the designated building typology, which is located in London, has been selected to drive the entire EIM process. Furthermore, the office building form category in England and Wales proposed by Shahrestani and his group shows sufficient data of existing building benchmarks, which can also be used for the test geometry preparation.

Further environmental simulation preparation will be discussed in the next chapter through the design benchmarks review, which should also base on the case brief such as internal condition, office schedule, and daily tasks in the workspace.

There are four office building types with different configuration will be demonstrated in the process as the most representative case study. The first case with open plan in the sidelit building form is codenamed OS. The second case with cellular strip plan in the sidelit building form is codenamed CS. The third case with open plan in the deep building form is codenamed OD. The fourth case with cellular strip plan in the deep building form is codenamed CD. Finally, the total floor plan area is 100 m², which is the same for all the case study. (Shahrestani. M, et al, 2014)

Table 3.2. Case basis (Source: Personal data, 2019) 1. Location 2. Climate classification Latitude: 51.09 N , Marine West Longitude: 0.11W , Coast Climate London, UK

3. Surrounding Urban context

4. Building Typology

5. Building form

6. Schedule

Office

Cellular / Open plan Sidelit / deep plan

Office open hour 9 am – 21 pm

7. Daily Task

8. building prototype

General Meeting/conference Gate lounge Circulation spaces

Total Floor area: 100 m² Number of floors: 10 Floors Test floor: 5th Floor

Layout plan Building form Case 2

Open plan

Cellular strip

Case 1: OS

Case 2: CS

Sidelit Width/Length = 1:4 = 5m:20m Total 20 staffs/floor

Width/Length = 1:1 = 5m:5m 5 staffs/room

Density=5 m²/person Case 4 Case 1

Case 3: OD

Case 4: CD

Deep plan Width/Length = 1:1 = 10m:10m Total 30 staffs/floor

Case 3

Figure 3.2. Office building form category (Source: Shahrestani. M, et al, 2014)

Density=3 m²/person

Width/Length = 1:1 = 5m:5m 5 staffs/room

4. PARAMETRIC FILTER DEVELOPMENT Overview of the parametric filter Base on the hypothesis of three EIM development frameworks, this chapter aims to establish an environmental indicators filter through the literature review that started from the project analysis. Furthermore, the following section shows three research topics to ensure the validity and reliability of the filter operation. First of all, the category of benchmarks as the integrated information of environmental design criteria and local construction regulation should respond to the project requirement. In addition, the design range of each environmental indicators should be identified through the programming of customised impact weight system, and also focus on the transformation of language. Furthermore, this research refers to manual data collection, which is one of the two tasks that should be conducted by an environmental specialist. Secondly, the definition of the customised impact weighting system should be clarified by comparing the potential outcome of the filter with the result of the environmental quality survey from the relevant research, and then prove the practicality of the system. Moreover, the comparison between the new design assessment and traditional building assessment method is necessary to confirm the practical improvement that mainly refers to the drawbacks of the evaluation target and their effectiveness in the designated design stage. As a result, the overall review of the completed parametric filter in the programming point of view will be presented to support the technical calculation within the EIM tool development in the next chapter. It should be noticed that the clarification is not only to analyse the composition of the weighting system but also to instruct the adjustment based on the different type of project basis, and then discuss the potential benefit of filter application in the end. In summary, this chapter will discuss the parametric filter development procedure, with the following key points: â&#x2014;? â&#x2014;? â&#x2014;?

Category of benchmarks. Assessment method and Customised impact weighting system. Characteristics of parametric filter.

4.1. Category of benchmarks Creating a category of benchmarks that based on the office building typology requirement and London environmental condition is the prerequisite to confirm the parametric filter basis.

Finally, the office building typology is different from each criterion, which means four cases that studied in the EIM demonstration should comply with the related description base on the selected benchmarks. For example, the laboratory is not included in the assumption of the project brief, so the case study will be determined as a general office building according to the BRE standard.

The first step of manual resource collection is to identify the benchmarks type from the integrated information that can promote the efficiency in the filter process.

The following table shows the integration of appropriate benchmarks that are further divided into two sections by archiving different colours. For example, the yellow bar indicates the selection of environmental design criteria, such as BREEAM, CIBSE guide A, and WELL building standard. In addition, the blue column means the selection of energy assessment criteria, such as CIBSE TM46 2008, UK Energy Consumption Guides (ECON19), and the Real Estate Environmental Benchmark (REEB). Another grey column is the resource will not be considered in the demonstration.

Base on the review of energy performance evaluation and technology reported by Borgstein and his team, it shows a clear evaluation system classification method through the difference of benchmarking methodology, the application result, and the main use country. (Borgstein. E, et al, 2016) In addition, the types of benchmarks should be differentiated by the use of building performance assessment method and its conducted stage in the project, as well as the field of environmental issue in each report will be the focus of the entire discussion. Also, environmental consultants will need to consider the definition of office buildings through the benchmark selection.

Moreover, according to the category composition, the next discussion will detail the environmental design and energy regulation of the selected system and make a comprehensive comparison of each environmental parameter that can be applied to the weighting system after decision making.

The most representative example will be the comparison between the Building Research Establishment Environmental Assessment Method (BREEAM) and the report from the Chartered Institution of Building Services Engineers (CIBSE). BREEAM is benchmarking the building performance with an operational rating system, which means the label is adjustable based on the project requirement and may have a specific result through the point calculation. However, CIBSE is defining the design quality by following the database and prototypical models, which means the result has corresponded to the existing comment. Not mention the pros and cons, both of them can be validated with proper operation in the project. Furthermore, in the research issue point of view, there are four kinds of criteria below, including the environmental design criteria and environmental assessment criteria. The difference in between is obvious that the design criteria are used to regulate the simulated result of environmental strategies in the early design process, while the assessment criteria are used to mark the final building performance. Also, energy assessment criteria, energy efficiency, and energy metering criteria will be complied to manage the final energy performance consumption after the construction stage.

Table 4.1.1. Category of benchmarks- office, UK (Source: Personal data, 2019) Benchmarks UK

Types

Application

Output

Office building typology in details

Building Research Establishment (BRE) BREEAM

Benchmarking + labelling (asset and operational rating)

Building assessment method Green building rating system

Environmental design criteria Energy criteria

Commercial- office: 1. General office buildings 2. Offices with research and development areas (category 1 laboratories only)

CIBSE Guide A

Database + Benchmarking (Prototypical models)

Environmental design criteria

Office: 1.executive Office: 2.general Office: 3.open-plan

Energy Efficiency in Buildings

Energy Efficiency

Building Energy Metering

Energy Metering

CIBSE Guide F CIBSE TM39:2006 CIBSE TM22:2006

Benchmarking (asset rating)

Energy Assessment and Reporting Methodology: Office Assessment Method

Energy Assessment

CIBSE TM46:2008

Benchmarking (Prototypical models) Benchmarking + labelling (asset and operational rating)

Energy Benchmarks

Energy criteria

A performance-based system for measuring, certifying, and monitoring features of the built environment that impact human health and wellbeing, through air, water, nourishment, light, fitness, comfort, and mind. Building energy use of nondomestic buildings

Environmental design criteria

1. Naturally ventilated 2. Mechanical ventilated 3.Mix ventilated

Energy Assessment , End energy consumption, by percentage Energy criteria: 1.Energy use indices (EUI) (kWh/m2/year) 2.Carbon dioxide emissions indices (CEI) (kgsCo2/m2/ye ar)

1. Office (natural ventilation) 2. Office (with A/C)

WELL Building Standard

UK Energy Efficiency Office (EEO)

Benchmarking (asset rating)

UK Energy Consumption Guides (ECON Guides or ECGs) ECON 19

Benchmarking (Prototypical models)

Building energy consumption of non-domestic buildings

Energy Consumption Guide for Offices (DEFRA)

Real Estate Benchmarking Operational environmental Environmental (asset rating) performance for commercial Benchmark property in the UK, based on the (REEB) performance of buildings â&#x20AC;&#x2DC;in-useâ&#x20AC;&#x2122;. Notice: * Yellow column means the environmental design criteria. * Blue column means the energy performance criteria. * Gray column means it will not be used in the report.

Energy criteria

1. Naturally ventilated cellular 2. Naturally ventilated open plan 3. Air conditioned standard 4. Air conditioned prestige General office

1. Naturally ventilated cellular: A simple building, often (but not always) relatively small and sometimes in converted residential accommodation. 2. Naturally ventilated open-plan: Largely open-plan, but with some cellular offices and special areas. Typical size ranges from 500 m2 to 4000 m2. 3. Standard air-conditioned: Largely purposebuilt and often speculatively developed. Typical size ranges from 2000 m2 to 8000 m2. 4. Air-conditioned, prestige: A national or regional head office, or technical or administrative center. Typical size ranges from 4000 m2 to 20 000 m2. 1.Air conditioned office 2.Non-air conditioned office

It should be noticed that all the criteria have their own requirement for each environmental indicator because of the difference in the benchmarking system that has been confirmed on the previous page.

In summary, it shows the selection of environmental indicators and their criteria basis should be properly controlled by environmental consultants to further meet the project requirement and specific request from clients.

Furthermore, the research of environmental indicators should be classified by the analysis topic to ensure a clear outcome. Besides, this arrangement also needs to be applied in the list of simulation template with environmental strategies and architecture design strategies in the EIM min map, and the classification of analytical tasks will remain the same between language translations in programming. In this way, the drawbacks of repeated calculation can be avoided in the workflow.

For example, the daylight factor and UDI should follow a more stringent standard to provide a better indoor lighting performance if the staff need to conduct a strong daily task in the workspace. On the contrary, the ventilation loss simulation does not have to confirm in the design process if the internal heat loss is not the main issue to focus on in the tropical area. Therefore, to have an initial image of the future living quality base on the comparison of environmental design benchmarks category, as well as the energy performance benchmarks, the new concept of assessment method will be provided with the customised impact weighting system in the next section.

Back to the discussion of the following table, there are five topics of environmental simulation, including lighting analysis, ventilation analysis, thermal comfort analysis, acoustic analysis, and energy analysis. In details, each topic has specific environmental issues that need to meet the environmental design principle through the simulated performance of the relevant environmental indicators. For example, lighting analysis aims to upgrade the daylight, sunlight and view out quality by improving several outputs, including daylight factor, useful daylight illuminance, daylight autonomy, daylight uniformity, and daylight glare index. Also, ventilation analysis aims to reduce the natural heat loss while enhancing the indoor air condition by confirming the performance of ventilation loss, air change rate, ventilation rate, and air quality. Moreover, the category also shows an overview of the performance achievement range for each indicator, which will vary according to different criteria. For example, the target of the daylight factor (DF) is greater than or equal to 2% reported by the Building Research Establishment (BRE). However, the DF design range is around 2% to 3% in the 80% area that base on the BREEAM International new construction 2018. Besides, the target of useful daylight illuminance (UDI) is 100 to 3000 (lux) in the 80% area in the BRE report. However, the range is from 300 to 500 (lux) in the CIBSE Guild A. It also happened in the different topic. In the thermal analysis, the office standard of indoor temperature is around 21 to 23 in the winter and 22 to 24 in the summer base one the CIBSE Guild A. However, the WELL building standard only shows the acceptability of classification rather than providing a numerical range directly.

Table 4.1.2. Category of benchmarks part1- office, UK (Source: Personal data, 2019) Environmental design benchmarks (Analysis task of Natural Strategies)

BRE

BREEAM International new construction 2018

CIBSE Guide A

WELL Building

1. Lighting analysis 1.1. Daylight

1.2. Sunlight

Daylight factor

ADF (%)

>=2%

2~3%, 80% area

Useful daylight illuminance Daylight autonomy

UDI (%)

100~3000 lux, 80% UDI

90~210~300 lux, 2000~2650 h/y, 80%UDI

300-500 lux

sDA (%)

300 lux, 50%h, 50% area

Daylighting uniformity Circadian Equivalent Melanopic Lux Daylight Glare index

(%)

>0.3-0.7%, 80% area

200~300 lux, 40~50%h, 30~70% area (Enhance: 300 lux, 50%h, 55-75% area) >=0.4%

(EML)

150-240 EML

DGI

16, 18, 20, 22, 24, 28

Annual sunlight exposure Annual probable sunlight hours

ASE (%)

1000 lux, 250 h, <10% area

ASPH (Annual) (h)

>=25%

ASPH (Winter) (h)

>=5% (21 September to 21 March), (<0.8 times of either period) 50% open area received >= 2 hours of sunlight (on 21 March) >=27% VSC, >=0.8 base case

1. <=7m, 20% 2. 8m-11m, 25% 3. 11m-14m, 30% 4. >=14m, 35% (Window to desk, window /wall ratio)

Overshadowing 1.3 Views out

Vertical sky component

VSC (%)

BREEAM International new construction 2018 2. Ventilation analysis 2.1. Nature heat Ventilation loss loss

3. Thermal analysis 3.1. Thermal comfort

CIBSE Guide A

WELL Building

W/m2K

Location of openable windows: One side: 1. Closed/ Closed, 0.3 2. Open/ Closed, 1 3. Open/ Open,3.3

More than one side: 1. Closed/ Closed, 0.6 2. Open/ Closed, 3.3 3. Open/ Open, 10

Air change rate

ACH

One side: 1. Closed/ Closed, 1 2. Open/ Closed, 3 3. Open/ Open, 10 (Day/ Night)

More than one side: 1. Closed/ Closed, 2 2. Open/ Closed, 10 3. Open/ Open, 30 (Day/ Night) Suggested air supply rate: 10 ach

1. ASHRAE 62.1-2010 2. CIBSE AM10 2005 Single sided ventilation, single opening W ≤ 2 H Single sided ventilation, double opening W ≤ 2·5 H, h approx. 1·5 m Cross ventilation W ≤ 5H

Infiltration rate

ACH

Air Quality

ppm or μg/m³

1a. 5% operable window area / floor area, cross ventilation for room with 7m15m depth 1b. Natural ventilation strategy for thermal comfort and ventilation rate 2. Natural ventilation strategy with 2 levels of user control

Air infiltration in winter: Office type 1: naturally ventilated up to 6 storeys (100–3000 m2), 9.3, 5.95, 3.5, 2.95. Office type 2: naturally ventilated up to 10 storeys (500–4000 m2), 5.95, 5.15, 3.5, 2.65. Office type 3: air conditioned up to 8 stores (2000–8000 m2), 4.3, 3.05, 2.65, 2.45. Office type 4: air conditioned HQ-type building up to 20 storeys, 3.8, 2.55, 1.95, 1.5, 1.65, 1.55. IDA1: High, <400, 700–750. IDA2: Medium, 400-600, 850–900. IDA3: Moderate, 600-1000, 1150–1200. IDA4: Low, >1000, 1550–1600. (Indoor CO2 concentrations/ ppm, Total ppm) (Outdoor CO2 concentrations/ ppm: 350-400)

Indoor temperature

(°C)

Thermal modelling with PMV, PPD

Naturally ventilated spaces: 1. 80%-100% acceptability limit (as per ASHRAE 55-2013) 2. Class I or II acceptability limit (as per EN 15251:2007)

Humidity

(%)

1. Office standard: winter operative temperature range 21-23 (1.2met, 0.85clo), Summer operative temperature range 22-24 (1.2met, 0.7clo) 2. Non-air conditioned office: warm summer 25, summer peak 28 /

1. Naturally ventilated 1a. Outdoor PM2.5, PM10, carbon monoxide and ozone levels within 4 km [2.5 mi] of the building at least 95% of all hours in the previous year 1b. Outdoor air meets the following thresholds as an average for the previous year: PM2.5 less than 25(35) μg/m³. PM10 less than 50(70) μg/m³

The modeled relative humidity levels in the space are between 30% and 60% for at least 98% of all business hours of the year.

Table 4.1.3. Category of benchmarks part 2- office, UK (Source: Personal data, 2019) Environmental design benchmarks (Analysis task of Natural Strategies) 4. Sound analysis 4.1. Noise

Sound Pressure Level

Reverberation time

SPL ( dB)

RT(60) (seconds)

Energy performance benchmarks ( Analysis task of Mechanical Strategies) 5. Energy analysis 5.1. Energy Break Heating Loads (kWh/m²·year)

5.2. Annual load

5.3.Occupant control

BREEAM International new construction 2018

CIBSE Guide A

1. General spaces (staffrooms, restrooms) ≤ 40 dB LAeqT 2. Single occupancy offices ≤ 40 dB LAeqT 3. Multiple occupancy offices 40-50 dB LAeqT 4. Meeting rooms 35-40 dB LAeqT 5. Receptions 40-50 dB LAeqT 6. Spaces designed for speech, e.g. teaching, seminar or lecture rooms ≤ 35 dB LAeqT 1. 50 m³, 0.4 s, 1 s 2. 100 m³, 0.5 s, 1.1 s 3. 200 m³, 0.6 s, 1.2 s 4. 500 m³, 0.7 s, 1.3 s 5. 1000 m³, 0.9 s, 1.5 s 6. 2000 m³, 1 s, 1.6 s (Room volume, speech, music) ECON19

Maximum permissible background noise levels: 1. Boardroom, large conference room, 25–30 2. Small conference room, executive office, reception room, 30–35 3. Open plan office, 35 4. drawing office, computer suite, 35–45

Point 3:

1. Conference rooms, no size, < 0.6s Classrooms, 2. < 280 m³[10,000 ft³], < 0.6s 3. 280 m³[10,000 ft³] - 570m³[20,000 ft³], 0.5~0.8s 4. > 570 m³[20,000 ft³], 0.6~1.0s

BREEAM International new construction 2018

1. Naturally ventilated cellular: 15, 28.7 / 2. Naturally ventilated open-plan: 15, 28.7 3. Standard air-conditioned: 18.4, 33.8 4. Air-conditioned, prestige: 20.3, 38.2 1. Naturally ventilated cellular: 0, 0 / 2. Naturally ventilated open-plan: 0.5, 1 3. Standard air-conditioned: 7.3, 16.1 4. Air-conditioned, prestige: 10.9, 21.3 (Good, Typical) 1. Naturally ventilated cellular 3. Standard air-conditioned Total gas or oil: 79, 151 Total gas or oil: 97, 178 Total electricity: 33, 54 Total electricity: 128, 226 2. Naturally ventilated open-plan 4. Air-conditioned, prestige Total gas or oil: 79, 151 Total gas or oil: 114, 210 Total electricity: 54, 85 Total electricity: 234, 358

Cooling Loads

(kWh/m²·year)

Annual Energy Loads Energy Use Intensity

(kWh/m²/year)

Energy cost

(£/m2/year)

1. Naturally ventilated cellular Total gas or oil: 1.03, 1.96 Total electricity: 16.2, 2.65 Total energy cost: 2.65, 4.61 2. Naturally ventilated open-plan Total gas or oil: 1.03, 1.96 Total electricity: 2.65, 4.17 Total energy cost: 3.68, 6.13

Annual emissions of CO2

(CEI)

Frequency of occupant hours

(%)

1. Naturally ventilated cellular: 32.2, 56.8 2. Naturally ventilated open-plan: 43.1, 72.9 3. Standard air-conditioned: 85, 151.3 4. Air-conditioned, prestige: 143.4, 226.1 /

3. Standard air-conditioned Total gas or oil: 1.26, 2.31 Total electricity: 4.99, 8.81 Total energy cost: 6.25, 11.12 4. Air-conditioned, prestige Total gas or oil: 1.25, 2.31 Total electricity: 9.13, 13.96 Total energy cost: 10.38, 16.27

WELL Building

BREEAM credits EPRNC Minimum standards Credit 1: 0.1, Credit 4: 0.4, Credit 6: 0.6, Credit 9: 0.9 AND zero net regulated CO2 emissions /

Notice: * Yellow column means the environmental design criteria. * Blue column means the energy performance criteria.

Point 1:

CIBSE TM46 2008

REEB

General office: Electricity typical: 95 Fossil-thermal typical: 120

Energy Intensity: 1.Air conditioned office Energy: 189-258 Electricity: 159-219 Fuels & thermals: 58-107

Energy Cost 1.Air conditioned office £18.21-£25.38 2.Non-air conditioned office: £8.39-£12.93 (good-typical practice)

General office: Electricity typical: 52.3 Fossil-thermal typical:22.8 Total: 75.1 2040 / 8760 (reference / maximum) Electricity typical: 107% Fossil-thermal typical: 44%

Point 2:

Average SPL: dBA, 45, 40, 35. dBC, 70, 65, 60. Max SPL: dBA, 55, 50, 45. dBC, 80, 75, 70. Average SPL: dBA, 50, 45, 40. dBC, 75, 70, 65. Max SPL: dBA, 60, 55, 50. dBC, 85, 80, 75. Average SPL: dBA, 55, 50, 45. dBC, 80, 75, 70. (Open Workspaces, Daytime, Night time)

2.Non-air conditioned office: Energy:100-144 Electricity:70-108 Fuels & thermals:54-85 (good-typical practice)

Table 4.2.1 Customised impact weighting system (Source: Personal data, 2019) Environmental design benchmarks - Analysis task of Natural Strategies New Scale BRE BREEAM 2018 1. Lighting analysis 15% 1.1. Daylight Useful daylight UDI (%) 6 x x illuminance Daylight autonomy sDA (%) 1.5 o

1.2 Views out 1.3. Sunlight

Daylight factor

ADF (%)

5.25

Daylighting uniformity Vertical sky component Annual probable sunlight hours

(%)

0.75

VSC (%)

0.45

ASPH (Annual) (h)

0.3

ASPH (Winter) (h)

0.3

(h)

0.45

Overshadowing 2. Ventilation analysis 2.1. Nature heat loss Ventilation loss

3. Thermal analysis 3.1. Thermal comfort

W/m2K

x x

Infiltration rate

ACH

2.25

Air change rate

ACH

2.25

Indoor temperature

(°C)

35% 31.5

Humidity

(%)

3.5

ECON19

4. Energy analysis 4.1. Annual load

Annual Energy Loads Cooling Loads Heating Loads

(kWh/m²/year)

35% 17.5

(kWh/m²·year) (kWh/m²·year)

8.75 8.75

o x

BREEAM 2018

CIBSE TM46 2008

REEB

Notice: * Yellow column means the environmental design criteria. * Blue column means the energy performance criteria. * Orange column means the selected benchmarks for each environmental parameters for the EIM demonstration in the report. * The figure of “o” or “x” means the performance of the test model can achieve each benchmark or not. Just a sample.

Figure 4.2.1. Parametric filter with customised impact weighting system (Source: Personal data, 2019)

WELL Building

15% 10.5

Energy performance benchmarks - Analysis task of Mechanical Strategies New Scale

4.2. Energy Break

CIBSE Guide A

4.2. Assessment method & weighting system A key point from the hypothesis of creating the parametric filter should be reaffirmed again that the environmental consultant is the leader of the EIM system, which means they have to inform the concern of environmental impacts while considering the client request as additional factors into the design process.

In the macroscopic, the percentage of thermal analysis is 35% which is the same as the ratio of energy analysis, while the lighting analysis is 20% lesser than the one in front, as well as the ventilation analysis. The point allocation shows the importance of thermal performance that can significantly influence the comfort of workspace and staff productivity, and also an emphasis on the indoor temperature with adequate clothing insulation (clo) in the office to reduce the demand of mechanical heating system for the winter in the UK. It presents the environmental target by customising living quality base on the project requirement and climate condition.

Furthermore, how to provide efficient strategies by following the environmental design principle to solve the specified issue while enhancing the overall performance and final evaluation level that can reach the expectation of living quality and future occupant satisfaction is the focus of the filter outcome.

In details, each environmental indicator in every analysis topic also has their percentage to impact the final score. For example, the UDI will contribute 6 point in to the scale, while the sunlight and views out analysis only can get the point around 0.3 to 0.45 from each relevant indicator, because the assumption of the less impact of surrounding context in the rural area far away from the city centre, but high concern of strong daylight which will cause the glare issue and reduce the UDI.

Therefore, the first step is to define the environmental issue base on the project brief and further emphasis on prioritising the environmental impact of each parameter. In other words, a new scale as the basis of customised impact weighting system will be presented base on the selection of environmental indicators and the corresponded criteria from the previous benchmarks category, which will show in percentage. The table nearby shows the sample of customised impact weighting system basis. In the demonstration, there are four analysis topics in the system with a total score is 100.

In addition, this table also clarifies the applied criteria for each parameter, which not only helps to settle up the target range in programming but also can be used to record the achievement of designated benchmarks.

To simplify the demonstration, there are four analysis topic in the system that the total score will be 100 by collecting all the result of performance simulation. Besides, the final score will be provided by the assessment of overall simulation results.

In summary, a new design assessment method has been confirmed based on the logical setting of customised impact weighting system.

Figure 4.2.2. EIM Assessment method & weighting system (Source: Personal data, 2019)

Different from the traditional evaluation system, the design assessment base on the customised impact weighting system has the progress of integration, labelling method, and the stage of execution.

It is evident that the high IEQ and low energy consumption will be a clear target to achieve base on the post-construction evaluation. However, the target of EIM system will be reaching the high score in the customised environmental scale.

In detail, the existing indoor environmental quality assessment method is used in the postconstruction stage to check the final building performance base on the classification of individual factors, as well as the building environmental assessment method. However, the evaluation in the early design stage is essential to confirm the design direction and further optimisation consistently, just like the description of PBD. Therefore, the design assessment should be practicable at all stage and always sharing the comment to project members.

The further discussion of design strategy efficiency that can be defined by the variable impact priority in the optimisation process base on the outputs of customised impact weighting system will be detailed in the demonstration chapter.

Moreover, it should be emphasised that parameters priority can significantly affect the final building performance and indoor environmental quality (IEQ). (Heinzerling, D, et al, 2013) According to the reference of IEQ survey, it shows the percentage of each analysis topic which will vary by the project location, the number of occupants, or the profession of respondents, which means customising the environmental target is necessary to reach a high living satisfaction. Besides, the report from Weng and Mui proves the comparison between energy performance assessment and IEQ acceptance in the office (Wong, LT., Mui, KW., 2009), which is not necessarily positively correlated.

Figure 4.2.3. Reference of comfort parameters vs energy consumption (Source: Wong, LT., Mui, KW., 2009)

Table 4.2.2. Reference of environmental quality survey in office (Source: Heinzerling, D, et al, 2013, Ncube, M., Riffat. S., et al, 2012, Middlehurst, G., et al, 2018) Literature review Location Number of occupants surveyed 1. Lighting analysis 2. Ventilation analysis 3. Thermal analysis 4. Sound analysis 5. Energy analysis

Taiwan

Research 1- overall Hong Beijing and Kong Shanghai 293 500 -

European Directive PMP-based

Research 2-UK Mix mode-UK Mechanical-UK

Research 3-UK UK

Granby House, Nottingham

27 subjects from 4 office in UK 0.267 0.126(IAQ)+0.11 7(VEN) 0.337 0.153

0.19

0.21

0.23

0.29

Leeds Town Centre House 0.16

0.34

0.25

0.14

0.23

0.2

0.36

0.24 0.23

0.31 0.24

0.38 0.27

0.29 0.25

0.12 0.39

0.3 0.18

12 professionals 0.19

N/A

Table 4.2.3. An energy performance assessment for indoor environmental quality (IEQ) acceptance in air-conditioned offices (Source: Wong, LT., Mui, KW., 2009) Benchmark j IEQ star rating IEQ index θ Thermal energy consummation Ec (kWhmˉ²yrˉ²) Energy to acceptance ration α Energy to IEQ improvement ratio β Percentage energy to IEQ improvement ratio β%

***** 0.95-0.97 1157 12.1 -

4 **** 0.93-0.95 1023 10.9 95 9.3%

3 *** 0.89-0.93 944 10.3 28 3%

0.79-0.89 906 10.7 5.6 0.6%

<0.01-0.79 844 14.9 2.2 0.3%

4.3. Characteristics of parametric filter According to the description of customised impact weighting system which shows the possibility of comprehensive design assessment base on the integration of environmental standard and the filtration of essential environmental indicators that respond to the project requirement, the foundation of parametric filter has been clarified and further visualised into the figure below.

It is evident that the progress of environmental simulation programme base on the parametric filter in the EIM system can solve the drawbacks of inefficient result calculation and delete the unwanted repetition instead of the old version of EST. The second focus will be the adaptability and flexibility of the customised scale. The parametric filter can be easily adjusted by the specialist such as adding a new subject or change the priority base on the different case basis, brief requirement, building typology, or climate classification.

The image of parametric filter shows a clear composition of environmental indicators, the selection of analysis tasks, and their percentage in the scale that can be customised for each case study. Moreover, it also shows the advantage of parametric filter application which not only can translating the design language into the simulation programme but also simplifying the overall research direction in the project which can be used to enhance the communication during the decision making.

For example, the residential house in the city centre will show a new scale with different percentage and indicator allocation because of the location will cause a new concern by urban context. Based on the above assumption, the daylight, and views out will be blocked by the taller building nearby and the density of metropolitan will change the skyline in this case. Therefore, the percentage of lighting analysis become higher in this residential house, which aims to solve the major environmental issue case by case.

In summary, there are two characteristics of the parametric filter. The first keyword is the accessibility of parameter details on the customised scale. The customised parametric filter shows a clear environmental impact priority and proportion of each environmental indicators in the percentage that can be used to define the multi-criteria design space while informing the design direction for all the project members.

Finally, the issue of programming and all the settings of simulation templates will be discussed in the next chapter based on the completed weighting system.

Figure 4.3. Adaptability of customised parametric filter (Source: Personal data, 2019)

5. TOOL DEVELOPMENT Overview of programming EIM tool development means to upgrade an existing EST which can realise not only the integrated BPS template but also the adaptable component in the programme. In this case, grasshopper with ladybug and honeybee plugin will be the appropriate option to input the parametric data. Furthermore, there are four stages in the development process based on the hypothesis of EIM framework for building design, including parametric modelling, project analysis, parametric filter, and parametric visualisation. In particular, the main focus in the workflow will be settling up the specific scale into the simulation template for each selected environmental indicator, while connecting the score calculation panel with the visual component for the final output. In short, preliminary preparation of tool includes the integration task of environmental benchmarks and also the clarification of EIM key concept which has been completed in the last chapter that refers to the design assessment method with customised impact weighting system. Therefore, this chapter aims to achieve the final research target of strategic visualisation while practicing the filtration task of customised scale. Finally, the outcome of the EIM system includes the overall score of environmental performance and the bar chart which shows the contribution from each indicator in percentage, will be discussed in brief. In summary, this chapter will discuss the tool development procedure, with the following key points: ● ● ● ● ●

Parametric modelling Project analysis Parametric filter Parametric visualisation Final review of tool outcomes

5.1. Parametric modelling stage This chapter aims to document the process of programming while defining input and output data and relevant component in four stages. First of all, in the parametric modelling stage, there are two tasks to create the test model as the basis of the simulation platform, which includes geometry preparation, materiality and construction layer establishment. It should be noticed that the building form input data is not only the experimental subject but also the controller of architectural design factors which can be easily optimised in the design process. For example, such as the adaptable number scale for the orientation, the number of floors, and the total gross floor area, as well as the setting of the Boolean toggle to switch the wind size and the level of height will be recorded as an environmental strategy. Furthermore, the construction database is settled by using the Honeybee plugin such as Energy Plus opaque material and Energy Plus window air gap that will be connected to the Energy Plus construction component and finally provide the thermal zone for the performance simulation. In addition, the material data can be called from the Energy Plus library. However, the self-defined material composition is clearer to be clarified as a potential design strategy.

● Geometry preparation

● Material & Construction layers

● Internal condition

● Thermal comfort, ● Parametric filter Ventilation, Energy from other analysis topics Simulation template ● Weighting system

● Weighting system

● Final score

● Strategic impact ● Overall weighting system

● Benchmark achievement ● Lighting Simulation Template

● Parametric filter from Lighting analysis topic

Figure 5.1. EIM tool development overview (Source: Personal data, 2019)

â&#x2014;? Geometry preparation â&#x2014;? Material & Construction layers

Figure 5.1.1. EIM tool development-parametric modelling stage (Source: Personal data, 2019)

5.2. Project analysis stage The internal condition and the selected simulation template should be completed in the project analysis stage. The internal condition includes the schedule of the occupied hour, equipment, lighting, and mechanical system application in a week, also the heating and cooling timetable should be defined with honeybee plugin continuously after providing the thermal model. Moreover, the category of BPS template should base on the integrated information from the previous research, such as the case assumption, office requirement, and the selection of environmental indicators that show the decision making base on the environmental design principle. Therefore, the selected BPS template such as lighting, ventilation, thermal comfort, and energy simulation will be conduct in the same platform to have an overall performance comparison. Also, the different environmental targets of each indicator according to the integrated research of design criteria will be completely set into the programme in the case need to change the applied benchmark. For example, the maximum and minimum threshold of BREEAM and CIBSE criteria is different for the UDI performance. So, both of them will share the same component for running daylight simulation, but the read annual result component will be separated.

● Internal condition

● Thermal comfort, Ventilation, Energy simulation template

● Lighting simulation template

Figure 5.1.2. EIM tool development-project analysis stage (Source: Personal data, 2019)

5.3. Parametric filter stage Thirdly, the parametric filter stage focus on the language translation of simulation result through the design assessment method, which means the programming of customised impact weighting system is essential for confirming the parameter priority in the technical calculation.

As a result of the test model, the range between 300 to 500 (lux) bases on the CIBSE guide A is the hardest to achieve, which only reach 20% of the target. However, the UDI range from 100 to 3000 (lux) in 80% area according to the BRE research is much easier to satisfy, which is 70% achievement in the same case. One thing should be clarified that the selection of benchmarks can not only be decided by its strictness, but also the detailed research of the criteria description and corresponded background.

Simply stated, there are three tasks in the process. The first step is to evaluate the numerical result of building performance simulation template base on the design range application of the designated benchmarks, and then multiplying the evaluation score with the allocated ratio of each environmental indicators. Finally, the total value of each analysis topic will be accumulated from the overall factor assessment outcome.

Furthermore, to calculate the final score of overall lighting analysis, the multiplication of relevant parameters with the weighting system should be conducted. For example, UDI simulation result accounts for 40% while the sDA is 10% of the lighting analysis, and then the overall addition will be the lighting analysis achievement, which is 82% in the test model.

Take the UDI design criteria as an example. It is evident that the difference of the threshold between the BRE research, BREEAM, and CIBSE guide A will influence the final score, which can also present the strictness of benchmarks.

● Parametric filter

● Weighting system ● Weighting system

● Parametric filter Figure 5.1.3. EIM tool development-parametric filter stage (Source: Personal data, 2019)

Figure 5.1.5. Final score of parameter filter presented by lunch box component (Source: Personal data, 2019)

● Final score

● Record of customised impact weighting system

● Strategic impact

● Benchmark achievement Figure 5.1.4. EIM tool development-parametric visualisation stage (Source: Personal data, 2019)

5.4. Parametric visualisation stage Furthermore, the graphic output from the tool mainly refers to the grasshopper chart form that can be generated through the visual component, which called lunch box component.

The final stage is parametric visualisation which aims to provide the overall assessment result base on the automatic calculation programme of parametric filter, and also the performance achievement details.

As a sample, the nearby screenshot of lunch box operational interface shows a clear design assessment result with the achievement proportion of environmental parameters. Therefore, the further result analysis that bases on the output bar chart is the focus in the next chapter.

In other words, the final outcome of the entire simulation template should include the final score of each case study, the final scale of customised impact weighting system, and the comparison of environmental impact priority that used to ensure the potential improvement space of each parameter. It shows the effectiveness of the parametric filter for confirming the environmental research direction and further promote the multi-criteria optimisation in the design process.

5.5. Final review of tool outcomes In summary, the outcome of EIM tool refers to the comprehensive environmental assessment result and final design achievement base on the customised scale, which still need the postprocessing analysis as well as the organisation of graphic category for the final report.

Back to the calculation process after the settlement of analysis topics individually. The score of each analysis topic should be multiplied by the customised proportion again and finally get the overall environmental assessment score from the total amount of them. For example, the result of lighting analysis and ventilation should multiply by 15%, and the thermal analysis and energy analysis output should multiply by 35% according to the customised weighting system that has been defined for the EIM demonstration.

As a sample, the figure bellow briefly reviews the overall result comparison in a single case study, which focus on the effectiveness of multi-criteria optimisation. It includes all the result in the design process, from the achievement of the basic model, through the improvement of strategies application, to the final performance of the best environmental solution.

It should be noticed that the calculation of each analysis topic from relevant environmental indicators and the calculation of the final score that include all the analysis result has been separated into two phases in the programme. In this way, the final report can provide sufficient environmental information in multiple aspects.

Furthermore, the progress of each environmental indicator can also be clarified at all the design stage rather than only presented the final score in the end, which is extremely important to define the usefulness of design strategies in multidimensional aspect while gathering the completed design information into the understandable bar chart which can share with project member. Therefore, the full record of EIM demonstration result will be discussed in the next chapter by following this analysis pattern.

Figure 5.2. Brief review of EIM tool outcomes (Source: Personal data, 2019)

6. EIM DEMONSTRATION Overview of EIM outcome analysis This chapter serves as the final stage of the EIM development framework, which aims to confirm the practicality of EIM system through the full case study according to the assumption in the methodology chapter.

base on the potential improvement space of each environmental indicators. In addition, the following layers present an overall building performance optimisation through the application of different architectural design factors, such as the adjustment of orientation and room volume, and further defining the usefulness of environmental strategies in a category.

Also, the post-processing analysis of EIM tool results is necessary to generate the environmental detail of design achievement, as well as the final graphic category that provided by the parametric visualisation stage should be considered into the report. Therefore, the following sections will discuss not only the final assessment score but also the environmental achievement research base on the preliminary review of tool outputs, and then to complete the EIM demonstration.

The last layer but not least, the multidimensional impact of environmental indicators from each environmental strategy should be clarified to ensure a correct design direction. In summary, the demonstration includes three tasks. First, defining the main issue by the basic achievement and the parametric impact priority.

Furthermore, the improvement layer as the simplified design process should be discussed respectively for environmental research outcomes in different stages, such as the project issue definer in the early stage and the solution effectiveness analysis after design. For example, the first layer refers to the preliminary simulation which aims to define the main environmental issue through the evaluation of potential building geometries and indoor configuration, as well as the output analysis

Secondly, defining the strategic effectiveness by the comparison of design factor improvements, while simplifying the optimisation process into the graphic category. Thirdly, clarifying the strategic impact priority through the growth rate of potential improvement space in the overall design process, and have a final review for the best solution.

Figure 6. Overview of EIM improvement layer (Source: Personal data, 2019)

6.1. Basic achievement & Issue definer The preliminary simulation results of the four basic geometry which has simplified the representative office building typology and configurations are the first EIM demonstration outcome, which should be analysed in the early design process. As the image below, it shows how to define the environmental issue for each project through the customised impact weighting system, which means the proportion of each environmental indicator as well as their respective percentage of achievement is the focus.

Secondly, the environmental impact of each case should be further discussed base on the multicriteria design assessment. In other words, the project issue can be confirmed through the contribution of each environmental indicator and their potential improvement space. More importantly, the higher the remained percentage of potential improvement space means the performance worsens. For example, the environmental issue in case 1 includes 85.72% of daylight uniformity, 70.2% of sunlight hour, 58.96% of indoor temperature, and 56.2% of heating loads, which also shows the impact priority base on the request of improvement.

In an overall view, the comparison of evaluation scores for each case study clarifies that building form optimisation research can significantly influence the final building performance, and also efficiently reduce energy consumption. According to the bar chart of basic achievement, it shows the case 1 gets the highest score of 67.73, while other case studies are around 56 to 60 points, which is obvious that the rectangular open plan office can perform better than the deep plan office.

In addition, other cases show a different priority of parameters impact that the heating loads become the focus to be solved, which the potential improvement space is higher than 92%, and the additional concern of sDA and UDI which need to be upgraded around 28 to 34%.

However, there are two points should be noticed. First, the building form operation may not be the most effective solution for all case studies, and this question should be confirmed by the final review of environmental strategies.

Therefore, the project target and design direction should be decided base on the environmental issue for each case and examine the potential solution in the next section.

Figure 6.1. Basic achievement & Issue definer (Source: Personal data, 2019)

6.2. Strategic effectiveness definer From the comparison of basic achievement through the evaluation of different building geometry, it is evident that office building with an open layout will result in a better performance in the beginning. However, additional improvement is still essential to solve the environmental issue and also upgrade the assessment score.

Furthermore, the next focus will be the aspect ratio improvement in the second layer that the simulation subject should base on the previous decision making of building orientation to control variables in the design process. The comparison shows the different setting of floor height plus window and wall ratio can also influence the score. The case with 3 m floor height and 10% WWR, as well as the case with 3.5 m floor height and 8% WWR, is 8 points higher than others.

Therefore, this section focus on the overall building optimisation by applying multiple environmental strategies, which mean the adequate control of architectural design factors in the programme. Furthermore, all the EIM simulation result in the design process should be organised into the graphic category, which includes the display of corresponded test model for a clear report.

In details, those two options result in a higher score is because of the better performance in the heating loads and infiltration loads issue, although it still has around 40% to 45% potential improvement space, the results of other cases are worse and out of the assessment range. However, the other case study shows the progress in the lighting aspect, include the 10% of daylight factor and 15% of UDI has been upgraded instead of the improvement of energy consumption.

The following table shows the assessment score of each design strategy that has been organised in order from the adjustment of orientation, volume, window and wall ratio (WWR), types of shading devices, materiality, and finally to the construction layer that base on the overview of improvement layer. In this way, the comparison between environmental strategies in a single improvement layer can be clarified more detailed and effective in specific research, as well as the impact of each environment indicators.

In addition, the materiality and construction improvement has more progress than the shading improvement in the fourth layer, which shows the 20% improvement of heating loads and 5% of indoor temperature when applied the new material of the external wall, which the U value is 0.35.

In short, the task of this stage is to clarify the best solution from each improvement layer respectively and keeping progress base on the continuous strategic decision making and then define the most effective strategy by the final review in the next section.

Although the above discussion only explains the simulation results in the design process of case 1 because of the limitation of thesis length, the emphasis of outcome analysis will be adaptable for each case study. Besides, the graphical category of other case studies will be recorded in the appendix.

Take case 1 as an example, the performance evaluation of rectangular open-plan office model in the first improvement layer shows the assessment scores from different orientations are similar, which means the strategic achievement of orientation improvement can reach around 67 points in general.

Moreover, the overall improvement category for the final review in the next section will clarify the optimisation results as well as the strategic impact priorities of all cases. In summary, the overall assessment score of environmental strategies is not equal to its effectiveness. The usefulness of strategies should base on the project issue that has been defined in the early stage, which means solving the key parameters can be regarded as an efficient design method to get a high score. It also evident that the priority of indicators in the customised impact weighting system is the main controller to influence the effectiveness of strategies.

However, slight progress can be proved by the strategic impact research of each environmental parameter, which shows the overshadowing issue has been solved more than 15% by turning the longer surface from the north side to the east-west side that can ensure more hour of sunlight exposure. Therefore, the best solution of orientation will be the third case that turns 90 degrees form the initial setting, which can get 67.77 in overall.

Table 6.2.1. Case 1 orientation improvement category (Source: Personal data, 2019) Improvement layer: Orientation

N: 0

NW: 45

Score

Bar chart

67.73

67.71

67.77

67.75

67.71

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure 6.2.1. Strategic achievement of orientation improvement: case1 (Source: Personal data, 2019)

W: 90

SW: 135

67.77

67.75

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure 6.2.2. Strategic impact of orientation improvement: case1 (Source: Personal data, 2019)

90.00 100.00

Table 6.2.2. Case 1 volume &WWR improvement category (Source: Personal data, 2019) Improvement layer: Volume & WWR

H3, WWR 10

Score

Bar chart

67.77 H3, WWR10

67.77

H3, WWR20

59.39

H3.5, WWR8.5 H3, WWR 20

59.39

67.38

H3.5, WWR10

59.95

H3.5, WWR20

57.91 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure 6.2.3. Strategic achievement of volume & WWR improvement: case1 (Source: Personal data, 2019)

H3.5, WWR8

67.38

H3.5, WWR 10

59.95

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

H3.5, WWR 20

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

57.91

Figure 6.2.4. Strategic impact of volume & WWR improvement: case1 (Source: Personal data, 2019)

90.00 100.00

Table 6.2.3. Case 1 shade improvement category (Source: Personal data, 2019) Improvement layer: Shade

Horizo ntal Shade depth: 1.5*1

Horizo ntal Shade depth: 0.3*3

Score

Bar chart

67.69

67.90

Horizon 1.5*1

67.69

Horizon 0.3*3

67.90

Horizon 0.3*6

67.40

Vertical

67.58

Grid

67.58

0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure 6.2.5. Strategic achievement of shade improvement: case1 (Source: Personal data, 2019)

Horizo ntal Shade depth: 0.3*6

67.40

Vertical Shade depth: 0.3*6

67.58

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

Grid Shade

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

67.58

Figure 6.2.6. Strategic impact of shade improvement: case1 (Source: Personal data, 2019)

90.00 100.00

Table 6.2.4. Case 1 materiality improvement category (Source: Personal data, 2019) Improvement layer: Materiality

U value: 1.9

U value: 1.49

Score

Bar chart

67.90

69.21

U value: 1.9

67.90

U value: 1.49

69.21

U value: 0.48

71.62

U value: 0.35

71.87 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure 6.2.7. Strategic achievement of material improvement: case1 (Source: Personal data, 2019)

U value: 0.48

71.62

U value: 0.35

71.87

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure 6.2.8. Strategic impact of material improvement: case1 (Source: Personal data, 2019)

90.00 100.00

Table 6.2.1. Case 1 overall improvement category (Source: Personal data, 2019) Overall improvement layer

S0: Basis

S1: W: 90

Score

Bar chart

67.73

67.77

67.73

67.77

67.90

71.87 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure 6.2.9. Overall strategic achievement: case1 (Source: Personal data, 2019)

S2: H3, WWR 10

67.77

S3: Horizo ntal Shade depth: 0.3*3

67.90

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

S4: U value: 0.35

10.00

20.00

30.00

40.00

50.00

60.00

70.00

71.87

Figure 6.2.10. Overall strategic effectiveness: case1 (Source: Personal data, 2019)

80.00

90.00 100.00

6.3. Final strategic achievement & performance The EIM outcome analysis includes three stages. It started from defining the environmental issue by the parametric impact priority that bases on the preliminary simulation result. The second step is clarifying the strategic effectiveness through the graphic category that used to compare each strategic achievement score and its impact of environmental indicators in the single improvement layer. Finally, this section aims to identify the strategic impact priorities from the entire optimisation process through the best solution final review of each improvement layer, as well as the growth rate analysis of key parameters with its potential improvement space.

In addition, more progress can be confirmed in case 3 analysis. In the beginning, the overall achievement score is 7 points lower than case 1, but the final result is approximately the same as case 1, which is 71.2. Moreover, the adjustment of orientation into north-west faced is the most effective strategies to improve 40% of heating loads and 9.22 % of overshadowing issue, as well as 8% improvement of the indoor temperature. Also, the usefulness of materiality is evident that can enhance the temperature performance up to 11.66%, while reducing the energy load up to 64.18%. On the contrary, the outcome analysis of cellular strip office layout in case 2 and case4 shows a different strategic impact priority. It shows a better lighting performance although the overall assessment score can only reach around 65 points in the end which is much lower than the open-plan office building.

Therefore, the comparison between the upgraded achievements from each improvement layer shows a simplified optimisation process, just like the following figures. For example, the best application of environmental strategies in the case 1 includes the east-west faced building orientation, 3m floor height with 10% WWR, horizontal shading devices with 0.3 depth internal, and the external wall with 0.35 U value. At the result, it shows the assessment score of 71.87, which is 4 points higher than before. Furthermore, it also proves that the adjustment of materiality is the most effective solution to promote the overall building performance, which shows more progress than others.

In addition, more design strategies can be considered in cellular layout design, such as applying a lager window and wall ratio at 20% in the third improvement and installing the horizontal shading devices with more partition in the fourth improvement layer, rather than just change the materiality. In details, it can improve more than 24% of sDA in case 2 or even up to 44.04% in the case 4, and more than 14% of daylight factor as well as the overshadowing issue. More importantly, both of them were involved in the top five issues, which means those strategies actually solve the problem in focus. Besides, the heating loads and indoor temperature also have progress around 23% and 8% respectively, even both of them still have a big potential improvement space around 60% to 70%.

Although the improvement between strategies is pretty slight in the general demonstration, however, the actual strategic impact on environmental indicators respectively is evident through the growth rate analysis. For example, even the performance of daylight uniformity did not have any progress after applying all the strategies in case 1, however, it obvious that the overshadow issue have 12.98% improvement by optimum the orientation. Furthermore, the other environmental issues also have significant progress from the initial condition by changing the U value of external wall materiality to 0.35, instead of U value 1.9. It includes 21.2% improvement of heating loads, and 3.19% improvement of indoor temperature performance.

In summary, the EIM outcome analysis presents the design information in several ways, from the main issue definer, parametric impact priority, strategic achievement score, and strategic impact of all simulation models that are included in the graphical category. Moreover, the overall research display like the figure below which shows not only the final review of strategic impact priority, but also the entire workflow of EIM optimisation, and further provides an efficient methodology of clarifying the improve direction to enhance the decision making in the design process.

CASE 1 outcome analysis

Figure 6.3.1. Final review of strategic impact priority in case 1 (Source: Personal data, 2019)

CASE 3 outcome analysis

Figure 6.3.3. Final review of strategic impact priority in case 3 (Source: Personal data, 2019)

CASE 2 outcome analysis

Figure 6.3.2. Final review of strategic impact priority in case 2 (Source: Personal data, 2019)

CASE 4 outcome analysis

Figure 6.3.4. Final review of strategic impact priority in case 4 (Source: Personal data, 2019)

7. CONCLUSION 7.1. Main findings In conclusion, EIM tool shows the advantage of confirming the design direction and customised environmental performance at all stage while responding to the client requirement during decision making. Furthermore, instead of traditional BPS and EST operation, the EIM system with simplified optimisation workflow and overall design assessment method can efficiently decode the simulation result by the specific EIM outcome analytical tasks.

As a design language translator, all the EIM simulation programme should following the integrated information of project analysis that bases on the setting of parameters proportion, which focus on connecting the client concern to the customised environmental target. Furthermore, it aims to share the design direction in the process by using the customised scale to present an image of evaluated living quality, which will be provided with each strategy in the graphical category.

In details, the following four statements are the main finding in the report.

Finally, the EIM outcome analysis method clarifies the overall environmental assessment tasks as well as potential improvement space of each environment indicators in the simplified optimisation process, which can facilitate the design efficiency between project members. In the completed EIM report, it should present the main issue of each project, the evaluation result of each test model, and finally providing effective design strategies by identifying the strategic effectiveness and strategic impact priority.

First of all, the EIM system can completely solve the drawbacks of EST while enhancing the communication between project members, and further respond to research purpose by practicing the concept of integration, filtration, and visualisation. No matter in which EIM development direction above, the initial preparation is the most important prerequisite should be achieved by the environmental specialists in the early stage, such as the project analysis and strategic definition for the integration, the selection of design benchmarks for the filtration, and the organisation of graphic category for the information visualisation. Otherwise, the EIM system cannot be established.

Moreover, the main image of EIM outcomes below shows the progress of integrated design information. The top left figure as the essence of the outcome analysis included the overall assessment result of each strategy and their environmental achievement, and also present the best solution in the optimisation process. However, the top right figure as a general combination of BPS outputs only can present the final simulation result of a single strategy, the specific output from each environmental indicators even harder to figure out the design direction in the future. The difference between EIM report and BPS outputs is obvious.

Secondly, the EIM tool development that based on the grasshopper platform and overall simulation template re-programming can successfully conduct the comprehensive environmental evaluation. It shows the effectiveness of EIM development frameworks that include defining the adaptable selection of environmental parameters in the multicriteria optimisation, clarifying the customised impact weighting system in the parametric filter, and finally practicing the evaluation score calculation and the visual component in the computer programme.

Therefore, the EIM tool can achieve a higher sustainable target with an adaptable and flexible evaluation system and also the accessibility of detail environmental information in the cooperative design process.

Thirdly, most importantly, the EIM core values include the overall assessment method that bases on the environmental indicators priority as well as the customised impact weighting system. Moreover, the parametric filter programming also can show a completed EIM structure that responds to those three development concepts.

Figure 7.1. Main image of EIM outcomes (Source: Personal data, 2019)

7.2. Reflection & Future step Based on the practicality of the EIM system that has been proven in the content, there are two improve direction should be conducted in the future. The first issue is the low usefulness of general design strategy assessment outcome, which can only result in an overall achievement around 7 points in the case study. However, the drawback of EIM demonstration in this thesis is because it only focuses on the completed system development, the trial operation of the tool, and the specific result interpretation, rather than figuring out a perfect environmental solution for the office building in the UK. Therefore, further EIM development with the critical implementation for building performance optimisation should consider the design principle moderately in the future. In addition, the next step in tool development should emphasis operational efficiency. The strategic evaluation in the report is generated individually by the repeated EIM tool operation. In order to facilitate the optimisation workflows, testing the multi-object optimisation component such as Octopus is essential to ensure the automatic calculation and control all the potential architecture design factors by running the entire template in once. Also, the Pareto-principle component such as Galapagos which can support the natural selection process may be useful to enhance the filtration tasks of the parametric filter in the programme and provide another possible integrated method by different chart analysis and outcome display.

8. REFERENCE Conference Paper: 1. Robinson, E., Hopfe, C.J. and Wright, J.A., Stakeholder decision making in Passivhaus design, Presented at the 7th Annual Symposium on Simulation for Architecture and Urban Design (SimAUD), University College, London, 16-18th May 2016. 2. Norouzi, N., Shabak, M., Embi,M.R.B., Khan, T.H., The architect, the client and effective communication in architectural design practice, Global Conference on Business & Social Science-2014, GCBSS-2014, 15th & 16th December, Kuala Lumpur, Procedia - Social and Behavioral Sciences, vol. 172, 2015, pp.635-642. 3. Naboni, E., Environmental Simulation Tools in Architectural Practice. The impact on processes, methods and design, PLEA2013 - 29th Conference, Sustainable Architecture for a Renewable Future, Munich, Germany, 10-12th September 2013. 4. Sadeghipour Roudsari, Mostapha & Pak, M., Ladybug: A parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association. Chambéry, France, 26-28th August 2013, pp. 3128-3135. 5. Aksamija, A. Methods for integrating parametric design with building performance analysis. ARCC Conference Repository. 2018. https://doi.org/10.17831/rep:arcc%y459 [Accessed at: 10/04/2019] Thesis: 6. Pourel, D., Multi-criteria optimisation for better indoor environment in UK homes, University of Westminster, Faculty of Architecture and Environmental Design Department of Architecture, 2017. 7. Berechikidze, S., Spatial mapping of thermal comfort i.e.comfort maps as design tool, University of Westminster, Faculty of Architecture and Environmental Design Department of Architecture, 2018. Journal Articles: 8. Konis, K., Gamas, A., Kensek, K. Passive performance and building form: An optimisation framework for early-stage design support. Solar Energy, vol. 125, 2016, pp. 161-179. 9. Touloupakia, E., Theodosioua, T., Optimisation of Building form to Minimize Energy Consumption through Parametric Modelling, Procedia Environmental Sciences, vol. 38, 2017, pp. 509-514. 10. Ekici, B., Cubukcuoglu, C., Turrin, M., Sariyildiz, I.S., Performative computational architecture using swarm and evolutionary optimisation: A review, Science Direct, Building and Environment, vol. 147, 2019, pp. 356–371. 11. Ø stergård, T., Jensen, R.L., Maagaard, S.E., Early Building Design: Informed decision-making by exploring multi-dimensional design space using sensitivity analysis, Science Direct, Energy and Buildings, vol. 142, 2017, pp.8-22. 12. Heinzerling. D., Schiavon. S., Webster. T., et al., Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme, Building and Environment, vol. 70, 2013, pp. 210-222. 13. Ncube. M., Riffat. S., Developing an indoor environment quality tool for assessment of mechanically ventilated office buildings in the UK e A preliminary study, Building and Environment, vol. 53, 2012, pp. 26-33. 14. Gary. M., Runming. Y., Lai. J., et al., A preliminary study on post-occupancy evaluation of four office buildings in the UK based on the Analytic Hierarchy Process, 2018, pp. 234-246. 15. Wong, LT., Mui, KW., An energy performance assessment for indoor environmental quality (IEQ) acceptance in air-conditioned offices, Energy conversion and management, vol. 50, issue. 5, 2009, pp. 13621367. 16.Shahrestani, M., Yao, R., Cook, G. K.,A review of existing building benchmarks and the development of a set of reference office buildings for England and Wales, Intelligent Buildings International Journal, vol. 6, no. 1, 2014, pp. 41-64. 17. Borgstein, E.H., Lamberts, R., Hensen, J. L. M., Evaluating energy performance in non-domestic buildings: a review, Energy and Buildings, vol. 128, 2016, pp. 734-755.

Website: 18. OCTOPUS. https://www.food4rhino.com/app/octopus [Accessed at: 18/04/2019]. 19. GALAPAGOS. https://www.grasshopper3d.com/group/galapagos [Accessed at: 18/04/2019]. Benchmarks and research paper: 20. Delva Patman Redler LLP, Hornsey High Street Environmental Statement Chapter.11 Daylight, Sunlight and Overshadowing, Building Research Establishment (BRE), London, 2017. 21. CIBSE, Environmental Design, Guide A, 8th Edition, Chartered Institution of Building Services Engineers, London, 2015. 22. Energy benchmarks CIBSE TM46: 2008, Chartered Institution of Building Services Engineers, London, 2008. 23. BREEAM International New Construction 2016, BREEAM Europe Commercial Assessor Manual, BRE Global Ltd, United Kingdom, 2017. 24. BREEAM UK new construction non-domestic buildings 2018, BRE Global Ltd, United Kingdom, 2018. 25. Energy Consumption Guide 19 (ECON19), Office Service Charge Analysis Research (OSCAR), Jones Lang LaSalle, London, 2000. 26. Real Estate Environmental Benchmarks (REEB), Better Buildings Partnership (BBP), London, 2017. 27. WELL building standard v2, International WELL Building Institute, 2018. https://dev-wellv2.wellcertified.com [Accessed at: 10/04/2019]

9. LIST OF FIGURES Figure 1.1.1. Table of EST today used in the analyzed architectural practices ............................ 2 Figure 1.1.2. Ideal framework for parametric and performance-based design .................................... 3 Figure 1.1.3. Drawbacks of environmental simulation tool ........................................................ 3 Figure 1.2.1. Relationship between designer and clients ..................................................................... 4 Figure 1.2.2. Relationship between designer and clients ..................................................................... 5 Figure 1.3. . Research question: Integration, Filtration, Visualisation ........................................... 6 Figure 1.4. Thesis scope and selected target ....... 7 Figure 2.1.3. Redefine multi-objective optimisation framework ............................................................ 12 Figure 2.1.1. The iterative framework. ................. 12 Figure 2.1.2. The design space in multi-dimension. ............................................................................. 12 Figure 2.2. Create environmental information filter ............................................................................. 13 Figure 2.3. Transfer parametric result into graphic category ............................................................... 14 Figure 2.4. EIM Framework for building design .. 15 Figure 3.1. EST basis: Rhino and Grasshopper .. 18 Figure 3.2. Office building form category............ 22 Figure 4.2.1. Parametric filter with customised impact weighting system ..................................... 30 Figure 4.2.2. EIM Assessment method & weighting system ................................................................. 31 Figure 4.2.3. Reference of comfort parameters vs energy consumption ............................................ 32 Figure 4.3. Adaptability of customised parametric filter ...................................................................... 33 Figure 5.1. EIM tool development overview ...... 35 Figure 5.1.1. EIM tool development-parametric modelling stage.................................................... 36 Figure 5.1.2. EIM tool development-project analysis stage ...................................................... 38 Figure 5.1.3. EIM tool development-parametric filter stage ............................................................ 39 Figure 5.1.5. Final score of parameter filter presented by lunch box component..................... 40 Figure 5.1.4. EIM tool development-parametric visualisation stage ............................................... 40 Figure 5.2. Brief review of EIM tool outcomes ... 41 Figure 6. Overview of EIM improvement layer ... 43 Figure 6.1. Basic achievement & Issue definer .. 44 Figure 6.2.1. Strategic achievement of orientation improvement: case1 ............................................ 46 Figure 6.2.2. Strategic impact of orientation improvement: case1 ............................................ 46 Figure 6.2.3. Strategic achievement of volume & WWR improvement: case1 .................................. 47 Figure 6.2.4. Strategic impact of volume & WWR improvement: case1 ............................................ 47 Figure 6.2.5. Strategic achievement of shade improvement: case1 ............................................ 48

Figure 6.2.6. Strategic impact of shade improvement: case1 ............................................ 48 Figure 6.2.7. Strategic achievement of material improvement: case1 ............................................ 49 Figure 6.2.8. Strategic impact of material improvement: case1 ............................................ 49 Figure 6.2.9. Overall strategic achievement: case1 ............................................................................ 50 Figure 6.2.10. Overall strategic effectiveness: case1................................................................... 50 Figure 6.3.1. Final review of strategic impact priority in case 1 .................................................. 52 Figure 6.3.3. Final review of strategic impact priority in case 3 .................................................. 52 Figure 6.3.2. Final review of strategic impact priority in case 2 .................................................. 53 Figure 6.3.4. Final review of strategic impact priority in case 4 .................................................. 53 Figure 7.1. Main image of EIM outcomes .......... 56 Figure a.1.1. Basic achievement of 4 test model 65 Figure a.1.2. Basic impact of 4 test model .......... 65 Figure a.2.1. Strategic achievement of orientation improvement: case 2 ........................................... 66 Figure a.2.2. Strategic impact of orientation improvement: case 2 ........................................... 66 Figure a.2.3. Strategic achievement of volume & WWR improvement: case 2 ................................ 67 Figure a.2.4. Strategic impact of volume & WWR improvement: case 2 ........................................... 67 Figure a.2.5. Strategic achievement of shade improvement: case 2 ........................................... 68 Figure a.2.6. Strategic impact of shade improvement: case 2 ........................................... 68 Figure a.2.7. Strategic achievement of materiality improvement: case 2 ........................................... 69 Figure a.2.8. Strategic impact of materiality improvement: case 2 ........................................... 69 Figure a.2.9. Strategic achievement of orverall improvement: case 2 ........................................... 70 Figure a.2.10. Strategic impact of overall improvement: case 2 ........................................... 70 Figure a.3.1. Strategic achievement of orientation improvement: case 3 ........................................... 71 Figure a.3.2. Strategic impact of orientation improvement: case 3 ........................................... 71 Figure a.3.3. Strategic achievement of volume & WWR improvement: case 3 ................................ 72 Figure a.3.4. Strategic impact of volume & WWR improvement: case 3 ........................................... 72 Figure a.3.5. Strategic achievement of shade improvement: case 3 ........................................... 73 Figure a.3.6. Strategic impact of shade improvement: case 3 ........................................... 73 Figure a.3.7. Strategic achievement of materiality improvement: case 3 ........................................... 74 Figure a.3.8. Strategic impact of materiality improvement: case 3 ........................................... 74

Figure a.3.9. Strategic achievement of overall improvement: case 3 ........................................... 75 Figure a.3.10. Strategic impact of overall improvement: case 3 ........................................... 75 Figure a.4.1. Strategic achievement of orientation improvement: case 4 ........................................... 76 Figure a.4.2. Strategic impact of orientation improvement: case 4 ........................................... 77 Figure a.4.3. Strategic achievement of volume & WWR improvement: case 4 ................................. 78 Figure a.4.4. Strategic impact of volume & WWR improvement: case 4 ........................................... 78 Figure a.4.5. Strategic achievement of shade improvement: case 4 ........................................... 79 Figure a.4.6. Strategic impact of shade improvement: case 4 ........................................... 79 Figure a.4.7. Strategic achievement of materiality improvement: case 4 ........................................... 80 Figure a.4.8. Strategic impact of materiality improvement: case 4 ........................................... 80 Figure a.4.9. Strategic achievement of overall improvement: case 4 ........................................... 81 Figure a.4.10. Strategic impact of overall improvement: case 4 ........................................... 81

10. LIST OF TABLES Table 2.1.1. Literature review: relevant terms of optimisation .......................................................... 11 Table 2.1.2. Literature review: relevant terms of optimisation .......................................................... 11 Table 3.1.1. EST basis & component: Rhino and Grasshopper ........................................................ 18 Table 3.1.2. EIM Mind map of development steps and tool use ......................................................... 20 Table 3.1.3. EIM Mind map of simulation tasks and design factors ...................................................... 21 Table 3.2. Case basis .......................................... 22 Table 4.1.1. Category of benchmarks- office, UK26 Table 4.2.1 Customised impact weighting system ............................................................................. 30 Table 4.2.2. Reference of environmental quality survey in office ..................................................... 32 Table 4.2.3. An energy performance assessment for indoor environmental quality (IEQ) acceptance in air-conditioned offices ...................................... 32 Table 6.2.1. Case 1 orientation improvement category ............................................................... 46 Table 6.2.2. Case 1 volume &WWR improvement category ............................................................... 47 Table 6.2.3. Case 1 shade improvement category ............................................................................. 48 Table 6.2.4. Case 1 materiality improvement category ............................................................... 49 Table 6.2.1. Case 1 overall improvement category ............................................................................. 50 Table a.1. Basic achievement category............... 65 Table a.2.1. Case 2 orientation improvement category ............................................................... 66 Table a.2.2. Case 2 volume & WWR improvement category ............................................................... 67 Table a.2.3. Case 2 shade improvement category ............................................................................. 68 Table a.2.4. Case 2 materiality improvement category ............................................................... 69 Table a.2.5. Case 2 overall improvement category ............................................................................. 70 Table a.3.1. Case 3 orientation improvement category ............................................................... 71 Table a.3.2. Case 3 volume & WWR improvement category ............................................................... 72 Table a.3.3. Case 3 shade improvement category ............................................................................. 73 Table a.3.4. Case 3 materiality improvement category ............................................................... 74 Table a.3.5. Case 3 overall improvement category ............................................................................. 75 Table a.4.1. Case 4 orientation improvement category ............................................................... 76 Table a.4.2. Case 4 volume & WWR improvement category ............................................................... 78 Table a.4.3. Case 1 shade improvement category ............................................................................. 79

Table a.4.4. Case 4 materiality improvement category .............................................................. 80 Table a.4.5. Case 4 overall improvement category ............................................................................ 81

11. APPENDICES

Table a.1. Basic achievement category (Source: Personal data, 2019) Improvement layer: Basic achievement

Case1:

Score

67.73 Case 1

Sidelet, Open plan

Case2:

Bar chart

57.19

67.73

Case 2

57.19

Case 3

60.17

Case 4

56.26

Sidelet,

0.00

Cellular

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.1.1. Basic achievement of 4 test model (Source: Personal data, 2019)

Case3:

60.17

Deep, Open plan

Case4: Deep,

56.26

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

Cellular

Figure a.1.2. Basic impact of 4 test model (Source: Personal data, 2019)

80.00

90.00 100.00

Table a.2.1. Case 2 orientation improvement category (Source: Personal data, 2019) Improvement layer: Orientation

N: 0

NW: 45

Score

Bar chart

57.19

57.28

57.19

57.28

57.13

57.26 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.2.1. Strategic achievement of orientation improvement: case 2 (Source: Personal data, 2019)

W: 90

57.13

SW: 135

57.16

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure a.2.2. Strategic impact of orientation improvement: case 2 (Source: Personal data, 2019)

90.00 100.00

Table a.2.2. Case 2 volume & WWR improvement category (Source: Personal data, 2019) Improvement layer: Volume & WWR

H3, WWR 10

H3, WWR 20

Score

57.28

58.73

Bar chart

H3, WWR10

57.28

H3, WWR20

58.73

H3.5, WWR8.5

56.51

H3.5, WWR10

57.45

H3.5, WWR20

58.08 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.2.3. Strategic achievement of volume & WWR improvement: case 2 (Source: Personal data, 2019)

H3.5, WWR8

56.51

H3.5, WWR 10

57.45

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

H3.5, WWR 20

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

58.08

Figure a.2.4. Strategic impact of volume & WWR improvement: case 2 (Source: Personal data, 2019)

90.00 100.00

Table a.2.3. Case 2 shade improvement category (Source: Personal data, 2019) Improvement layer: Shade

Horizo ntal Shade depth: 1.5*1

Score

60.05

Bar chart

Horizon 1.5*1

60.05

Horizon 0.3*3

59.45

Horizon 0.3*6

Horizo ntal Shade depth: 0.3*3

59.45

62.02

Vertical 61.54 Grid 61.56 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.2.5. Strategic achievement of shade improvement: case 2 (Source: Personal data, 2019)

Horizo ntal Shade depth: 0.3*6

62.02

Vertical Shade depth: 0.3*6

61.54

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

Grid Shade

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

61.56

Figure a.2.6. Strategic impact of shade improvement: case 2 (Source: Personal data, 2019)

90.00 100.00

Table a.2.4. Case 2 materiality improvement category (Source: Personal data, 2019) Improvement layer: Materiality

U value: 1.9

Score

Bar chart

62.02 U value: 1.9

62.02

U value: 1.49

63.96

U value: 0.48

U value: 1.49

63.96

65.57

U value: 0.35

66.07 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.2.7. Strategic achievement of materiality improvement: case 2 (Source: Personal data, 2019)

U value: 0.48

65.97

U value: 0.35

66.07

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure a.2.8. Strategic impact of materiality improvement: case 2 (Source: Personal data, 2019)

90.00 100.00

Table a.2.5. Case 2 overall improvement category (Source: Personal data, 2019) Overall improvement layer

S0: Basis

Score

Bar chart

57.13 S0

57.13

57.28

58.73

S3 S1: NW: 45

62.02

57.28 S4

66.07 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.2.9. Strategic achievement of orverall improvement: case 2 (Source: Personal data, 2019)

S2: H3, WWR 20

58.73

S3: Horizo ntal Shade depth: 0.3*6

62.02

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

S4: U value: 0.35

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

66.07

Figure a.2.10. Strategic impact of overall improvement: case 2 (Source: Personal data, 2019)

90.00 100.00

Table a.3.1. Case 3 orientation improvement category (Source: Personal data, 2019) Improvement layer: Orientation

N: 0

Score

Bar chart

60.17

NW: 45

67.07

60.17

67.07

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.3.1. Strategic achievement of orientation improvement: case 3 (Source: Personal data, 2019)

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure a.3.2. Strategic impact of orientation improvement: case 3 (Source: Personal data, 2019)

90.00 100.00

Table a.3.2. Case 3 volume & WWR improvement category (Source: Personal data, 2019) Improvement layer: Volume & WWR

H3, WWR 10

Score

67.07

Bar chart

H3, WWR10

67.07

H3, WWR20

54.71

H3.5, WWR8.5 H3, WWR 20

54.71

66.99

H3.5, WWR10

59.93

H3.5, WWR20

60.82 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.3.3. Strategic achievement of volume & WWR improvement: case 3 (Source: Personal data, 2019)

H3.5, WWR8

66.99

H3.5, WWR 10

59.93

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

H3.5, WWR 20

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

60.82

Figure a.3.4. Strategic impact of volume & WWR improvement: case 3 (Source: Personal data, 2019)

90.00 100.00

Table a.3.3. Case 3 shade improvement category (Source: Personal data, 2019) Improvement layer: Shade

Horizo ntal Shade depth: 1.5*1

Horizo ntal Shade depth: 0.3*3

Score

Bar chart

67.20

67.34

Horizon 1.5*1

67.20

Horizon 0.3*3

67.34

Horizon 0.3*6

67.00 66.96

Vertical

66.95

Grid 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.3.5. Strategic achievement of shade improvement: case 3 (Source: Personal data, 2019)

Horizo ntal Shade depth: 0.3*6

67.00

Vertical Shade depth: 0.3*6

66.96

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

Grid Shade

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

66.95

Figure a.3.6. Strategic impact of shade improvement: case 3 (Source: Personal data, 2019)

90.00 100.00

Table a.3.4. Case 3 materiality improvement category (Source: Personal data, 2019) Improvement layer: Materiality

U value: 1.9

Score

Bar chart

67.34 U value: 1.9

67.34

U value: 1.49

68.55

U value: 0.48 U value: 1.49

68.55

71.04

U value: 0.35

71.20 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.3.7. Strategic achievement of materiality improvement: case 3 (Source: Personal data, 2019)

U value: 0.48

71.04

U value: 0.35

71.20

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure a.3.8. Strategic impact of materiality improvement: case 3 (Source: Personal data, 2019)

90.00 100.00

Table a.3.5. Case 3 overall improvement category (Source: Personal data, 2019) Overall improvement layer

S0: Basis

Score

Bar chart

60.17 60.17

S1: NW: 45

67.07

67.34

67.07 S4

71.20 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.3.9. Strategic achievement of overall improvement: case 3 (Source: Personal data, 2019)

S2: H3, WWR 10

67.07

S3: Horizo ntal Shade depth: 0.3*3

67.34

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

S4: U value: 0.35

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

71.20

Figure a.3.10. Strategic impact of overall improvement: case 3 (Source: Personal data, 2019)

90.00 100.00

Table a.4.1. Case 4 orientation improvement category (Source: Personal data, 2019) Improvement layer: Orientation

N: 0

NW: 45

W: 90

Score

Bar chart

56.26 N

56.26

56.30

56.19

56.45

56.59

56.75

56.53

56.45

56.30

56.19

0.00

SW: 135

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

56.45

Figure a.4.1. Strategic achievement of orientation improvement: case 4 (Source: Personal data, 2019)

90.00 100.00

S: 180

56.59 UDI SDA ADF DFU VSC SH

SE: 225

56.75

OS IL IR AR T H

E: 270

56.53

ANL CL HL 0.00

NE: 315

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

56.45

Figure a.4.2. Strategic impact of orientation improvement: case 4 (Source: Personal data, 2019)

90.00 100.00

Table a.4.2. Case 4 volume & WWR improvement category (Source: Personal data, 2019) Improvement layer: Volume & WWR

H3, WWR 10

Score

56.75

Bar chart

H3, WWR10

56.75

H3, WWR20

H3, WWR 20

58.27

58.72

H3.5, WWR8.5

56.15

H3.5, WWR10

56.86

H3.5, WWR20

57.59 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.4.3. Strategic achievement of volume & WWR improvement: case 4 (Source: Personal data, 2019)

H3.5, WWR8

56.15

H3.5, WWR 10

56.86

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

H3.5, WWR 20

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

57.59

Figure a.4.4. Strategic impact of volume & WWR improvement: case 4 (Source: Personal data, 2019)

90.00 100.00

Table a.4.3. Case 1 shade improvement category (Source: Personal data, 2019) Improvement layer: Shade

Horizo ntal Shade depth: 1.5*1

Score

Bar chart

60.77 Horizon 1.5*1

60.77

Horizon 0.3*3

Horizo ntal Shade depth: 0.3*3

58.55

Horizon 0.3*6

61.14

Vertical

61.06

Grid

61.06 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.4.5. Strategic achievement of shade improvement: case 4 (Source: Personal data, 2019)

Horizo ntal Shade depth: 0.3*6

61.14

Vertical Shade depth: 0.3*6

61.06

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

Grid Shade

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

61.06

Figure a.4.6. Strategic impact of shade improvement: case 4 (Source: Personal data, 2019)

90.00 100.00

Table a.4.4. Case 4 materiality improvement category (Source: Personal data, 2019) Improvement layer: Materiality

U value: 1.9

Score

Bar chart

61.14 U value: 1.9

61.14

U value: 1.49

62.91

U value: 0.48

U value: 1.49

62.91

64.97

U value: 0.35

65.07 0.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Figure a.4.7. Strategic achievement of materiality improvement: case 4 (Source: Personal data, 2019)

U value: 0.48

64.97

U value: 0.35

65.07

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Figure a.4.8. Strategic impact of materiality improvement: case 4 (Source: Personal data, 2019)

90.00 100.00

Table a.4.5. Case 4 overall improvement category (Source: Personal data, 2019) Overall improvement layer

S0: Basis

Score

Bar chart

56.26 S0

56.26

56.75

58.27

S3 S1: SE: 225

61.14

56.75 S4

65.07 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00 100.00

Figure a.4.9. Strategic achievement of overall improvement: case 4 (Source: Personal data, 2019)

S2: H3, WWR 20

58.27

S3: Horizo ntal Shade depth: 0.3*6

61.14

UDI SDA ADF DFU VSC SH OS IL IR AR T H ANL CL HL 0.00

S4: U value: 0.35

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

65.07

Figure a.4.10. Strategic impact of overall improvement: case 4 (Source: Personal data, 2019)

90.00 100.00