AI in construction project management to reduce the risk of cost overruns in projects by Suhani Sharma

AI IN THE FIELD OF RISK ASSESSMENT TO AVOID COST OVERRUNS IN CONSTRUCTION

SUHANI SHARMA W1894225

MSc CONSTRUCTION PROJECT MANAGEMENT

Date- 31 August 2023

UNIVERSITY OF WESTMINSTER School of Applied Management

ABSTRACT Cost overruns have frequently been a key concern in building processes. Artificial intelligence has stepped in as a tool/ technology which can be used to avoid this threat in the construction sector. The aim of this study is to probe AI in the field of threat assessment models to avoid cost overruns in construction systems. The construction assiduity is changeable and systems depend on colourful factors for successful completion, without running over budget or causing detainments. The following objectives were linked to meet the end results • Identify which AI tools and ways can be applied in the construction assiduity • Is a mongrel system using AI more effective to address system queries more effectively and ameliorate decision-making in construction systems, which other styles can be employed to handle the design complexity and danger interdependency networks in the system under high-cost query. • Critically probe what challenges AI faces in the field of threat assessment to avoid cost overruns in construction systems The objects were linked keeping in mind the oscillations of the assiduity. The literature review addresses the future of AI, safety in construction, and colourful models that can be employed to avoid cost overruns. Semi-structured interviews, check forms and case studies were conducted to understand the challenges and success of AI. The findings show that artificial intelligence has enormous implicit to revolutionise threat assessment, cost operation, and assistive design charges in construction, paving the way for more effective, cost-effective, and successful systems.

CONTENTS ABSTRACT ........................................................................................................................... ii CONTENTS.......................................................................................................................... iii LIST OF TABLES AND ILLUSTRATIONS .................................................................................vi DECLARATION ....................................................................................................................vii PREFACE ............................................................................................................................ viii ▪

CHAPTER 1 .................................................................................................................. 1

INTRODUCTION .......................................................................................................... 1 1.1

Aim of the study: ................................................................................................ 1

1.2

Objective of the study: ....................................................................................... 1

1.3

Rationale:............................................................................................................ 1

1.4

Scope of work: .................................................................................................... 1

1.5

Structure of work: .............................................................................................. 2

1.6

Introduction to the study : ................................................................................. 2

▪

CHAPTER 2 .................................................................................................................. 4

LITERATURE REVIEW:.................................................................................................. 4 2.1 The Future of AI in Construction: Potential Growth and Technological Advancements ................................................................................................................ 4 2.2 Improving Cost Prediction in Construction Projects: Challenges and Approaches ..................................................................................................................... 6 2.3 Enhancing Construction Management for Improved Productivity and Economic Growth ........................................................................................................... 7 2.4 Ensuring Safety in the Construction Industry: Importance of Suitable Construction Management Practices and Architectural Design ................................... 8 2.5 AI-Based Approaches for Risk Assessment and Cost-Risk Analysis in Construction Projects ..................................................................................................... 9 2.6 Addressing Challenges and Unlocking the Potential of AI-based Risk Assessment for Cost Overrun Prevention in Construction Projects ........................... 10 2.7 Managing Complexity and Uncertainty in Construction Projects: Contingency Planning and Cost Control ............................................................................................ 12 2.8

Neutral network ............................................................................................... 13

2.9 AI-Based Approaches for Risk and Uncertainty Analysis in Construction Projects: Hybrid Techniques, Risk Interdependencies, and Failure Mode and Effect Analysis (FMEA) ............................................................................................................ 13

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2.10 AI-Based Approaches for Risk and Uncertainty Analysis in Construction Projects: Fuzzy Logic, Neural Networks, Genetic Algorithms, and Object-Oriented Approach ...................................................................................................................... 14 2.11

Fuzzy logic ......................................................................................................... 14

2.12

Genetic algorithms ........................................................................................... 15

2.13

Object-oriented approach ................................................................................ 15

2.14 Integration of Fuzzy Logic and Neural Networks for Intelligent Systems: Fuzzy Neural Networks (FNN) as a Unified Approach ........................................................... 17 2.15 AI-Powered Techniques for Reducing Construction Cost Overruns: Predictive Analytics, Computer Vision, Natural Language Processing, Virtual Reality, and Augmented Reality ....................................................................................................... 18 2.16

summary of literature review .......................................................................... 22

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CHAPTER 3 ................................................................................................................ 28

RESEARCH DESIGN AND METHODOLOGY ................................................................ 28 3.1

Triangulation ..................................................................................................... 28

3.2 Research Approach and Data Collection Methods for Assessing AI and Risk Assessment in Building Projects .................................................................................. 29 3.3 AI Applications for Risk Assessment and Cost Overrun Prevention in Building Projects: Examples of Companies and Technologies................................................... 30 ▪

CHAPTER 4 ................................................................................................................ 32

DATA COLLECTION .................................................................................................... 32

4.1 CASE STUDIES ............................................................................................................. 32 4.2 INTERVIEW.................................................................................................................. 37 4.3

SURVEY ................................................................................................................. 40

4.4 limitations of data collection ..................................................................................... 40 ▪

CHAPTER 5 ................................................................................................................ 41

SECONDARY DATA ..................................................................................................... 41

▪

5.1

Model of the Random Forest ........................................................................... 41

5.2

The Hybrid RF-GA Model.................................................................................. 41

5.3

Impact on mega projects .................................................................................. 42

5.4

Advantages of AI in project management ....................................................... 43

CHAPTER 6 ................................................................................................................ 46 6.1 FINDINGS ................................................................................................................ 46

6.1.1Factors Contributing to Cost Overruns in Construction Projects ............................ 47 ▪

CHAPTER 7 ................................................................................................................ 54 7.1 CONCLUSION .......................................................................................................... 54

7.2 RECOMMENDATIONS ............................................................................................. 55 7.3 LIMITATIONS OF THE STUDY................................................................................... 56 ▪

CHAPTER 8 ................................................................................................................ 57 ▪

8.1 Reference list .................................................................................................. 57

8.2 Bibliography ............................................................................................................ 69 Appendix 1 ........................................................................................................................ 72 Appendix 2 ........................................................................................................................ 74 Appendix 3 ........................................................................................................................ 76

LIST OF TABLES AND ILLUSTRATIONS Fig 1. Construction technology map (McKinsey & Company, 2020)………………………………5 Fig 2 Bid structure and analysis in projects (Lu et al., 2015; Hashemi et al, 2020)…………………………………………………………………………..7 Fig 3 Construction 4.0+AI techniques (Baduge et al., 2022)……………………………………….16 Fig 4 Components, types, and subfields of AI (Regona et al., 2022)…………………………..17 Fig 5 FMEA system (Abdelgawad and Fayek, 2010)……………………………………………………20 Fig.6 Frequency of papers from 1960 to 2020 (Abioye et al., 2021)…………………………..21 Table 1, summary of literature review …………………………………………………………………22-27 Fig 7 Research methodology (Elmousalami, 2020)……………………………………………………28 Fig 8 Triangulation Method and research workflow (Priatmoko et al., 2021)……………29 Fig 9 The Oasia Downtown Hotel (Oasiahotels.com ,2023)………………………………….…..35 Fig 10 The Crossrail maintenance strategy (Crossrail Learning Legacy, n.d.)………….….36 Fig 11 Western Sydney Airport, Australia (ArchDaily, 2019)……………………………….….…37 Fig 12 Hybrid artificial intelligence model Random Forest and Genetic Algorithm (RF-GA) structure (Yaseen et al., 2020)…………………………………………………….42 Fig. 13 advantages and limitations of subfields of AI (Abioye et al., 2021,p1)……………45 Fig 14 Review of past practices of cost model development (Elmousalami,2020)……..46 Table 2 Responses from the survey form…………………………………………………………………..49 Fig 15 Labour productivity, gross value added per hour worked (McKinsey & Company, 2020; Between the Poles, 2023)………………………………………………………………………………..51 Fig 16 The construction industry is least digitilised (mckinsey global institute ,2016; Poinet, 2020)………………………………………………………………………………………………………………52

DECLARATION I hereby certify that all material in this dissertation which is not my work has been identified through the proper use of citations and references. I also confirm that I have fully acknowledged by name all of those individuals and organizations that have contributed to the research for this dissertation/work-related project. I further declare that this dissertation has not been accepted in part or in full for any other degree, nor is it being submitted currently for any other degree. The dissertation contains 11359 words, exclusive of diagrams, tables, bibliography and appendices. I confirm that a digital copy of this dissertation/work-related project may be made available to future students of the University of Westminster. Student’s name SUHANI SHARMA Student’s signature Suhani Sharma Date of declaration 31 August 2023

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PREFACE The dissertation dives into the exciting subject of artificial intelligence (AI) in risk assessment models to reduce cost overruns in building projects. The incorporation of artificial intelligence (AI) offers captivating solutions to enhance decision-making processes and address intricate risk interdependencies within a sector burdened by numerous obstacles and uncertainties. The dissertation is about the interesting and ever-evolving artificial intelligence to seek its use in assessing the risk of cost overruns in building projects. The construction industry is complex and unpredictable, also it poses a lot of uncertainties, AI can help by providing solutions through enhanced decision-making processes. The goal of this study is to investigate the various AI tools and approaches that may be used in the construction sector and to assess their usefulness in dealing in regard to system uncertainty. The study will also look into hybrid AI technologies and their effectiveness in managing cost uncertainty while taking into account the interconnection of hazards in projects. Furthermore, it explores critically the issues that AI faces in the field of risk assessment, particularly in the context of preventing cost overruns. The goal of the dissertation is to identify and investigate various tools and techniques in AI that can be employed for uncertainties in projects. The study mainly focuses on Hybrid AI models which means Human + machine model to curb the problems on site especially that of cost overruns. The study will also look into challenges faced by such models and advantages of using an AI for accessing risks. A detailed technique has been used to achieve these goals. It entails discovering construction businesses that use AI in their projects and collecting important input on the difficulties they have experienced. Questionnaires and interviews with industry specialists will be undertaken to gather insight into the fuzzy hybrid methodologies often utilised in risk assessment. Case studies of successful AI applications in building projects will also be reviewed to benefit from actual experiences. The research will also include a literature review to determine the key factors affecting project risk, with an emphasis on cost overruns.

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Throughout this study, significant enterprises and organisations that use AI in construction risk assessment will be highlighted. Oracle Construction and Engineering, Sky catch, Construct Secure, Autodesk, VIMaec, and Hilti are among the early adopters of AI-powered technology to reduce risks and enhance project outcomes. This study aims to contribute to existing knowledge by focusing on the growing popularity of AI-based hybrid solutions in construction risk management. “By examining and merging existing AI algorithms used to assess cost-risk, the research intends to provide insights into capturing complexity and risk interdependencies when there is a lot of ambiguity" (Donato, 2022). As the global construction market is likely to develop significantly in the next years, with AI playing a crucial role in enhancing productivity, The goal of this study is to give insights on possible benefits and problems of using AI in risk assessment for construction projects. This dissertation intends to give significant ideas and insights to industry professionals, researchers, and policymakers by bridging the gap between theory and practice. In this research journey, the author expresses immense gratitude towards their advisers, mentors, and coworkers for providing encouragement, guidance, and invaluable insights. Their knowledge and support played a vital role in constructing this dissertation. Their contributions greatly contributed to its successful completion. I am also appreciative of the construction businesses and professionals who freely offered their experiences and views, enriching our study. It is my aim that this dissertation will be useful to anybody interested in the interface of artificial intelligence, risk assessment, and building projects. May it catalyse more studies and breakthroughs in this fast-growing sector. Suhani Sharma

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▪ CHAPTER 1 1

INTRODUCTION

1.1 Aim of the study: Investigating AI in the field of risk assessment model to avoid cost overrun in construction projects.

1.2

Objective of the study: •

Identify which AI tools and techniques can be applied in the construction industry.

•

Is hybrid method using AI more efficient to address system uncertainty more effectively and improve decision making in construction projects, which other methods can be employed to handle project complexity and risk interdependency networks in a system with significant cost uncertainties?

•

Critically investigate what challenges does AI face in the field of risk assessment to avoid cost overrun in construction projects.

1.3

Rationale:

Construction projects often tend to go overbudget due to several reasons such as scope creep, schedule changes, material procurement, inadequate stakeholder management, communication errors etc. With the rapid growth in artificial intelligence models, (AI)based hybrid solutions have grown in popularity in risk management practices for construction throughout the past few decades. The goal is to identify various models and tools in AI that can prevent cost overruns.

1.4 Scope of work: The scope of work is limited to firms and individuals using AI in the industry, the limitation to the study is posed due to the fact that AI is still a developing industry in construction and the industry is slow in adapting the technological change. Surveys and interviews will be conducted to evaluate the extent of efficiency, case studies will also be looked at where AI has already been employed to see how successful it has been in the past and how it can be put to use in upcoming ventures. The use of AI in construction will not only reduce cost overruns but will make the overall industry smoother and more

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efficient and reduce delays in projects, wastage on sites will also reduce as AI continues to develop further.

1.5

Structure of work:

The paper follows the following structure of work: Introduction Literature review Research Design and Methodology Data Collection Secondary Data Findings Conclusion, recommendation, and limitations References

1.6 Introduction to the study : The construction business is notorious for its inherent complexity, uncertainties, and hazards, which frequently result in cost overruns and project delays. There is a rising interest in harnessing artificial intelligence (AI) in the field of risk assessment to reduce these problems and enhance project results (Afzal et al, 2019). “Under high uncertainty, artificial intelligence may improve decision-making processes, capture complexity, and manage risk interdependencies” (Afzal et al, 2019; Abdelgawad & Fayek, 2010). AI can give significant insights and prediction skills that enable proactive risk management and cost reduction by utilising powerful algorithms and analysing massive amount of data.

Cost forecasting is an important activity in construction projects since it acts as a prelude to budgeting and resource allocation (Schwalbe & Wahl, 2020). Traditional cost prediction methods, on the other hand, frequently fall short of adequately portraying the dynamic character of building projects, especially when the project scope is uncertain or prone to change (Spalzzi, 2001). “Cost overruns and financial inconsistencies might emerge from inadequate and imprecise cost predictions “(Lu et al., 2015). Researchers and practitioners have resorted to AI-based ways to solve this issue,

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which can use machine learning and mathematical models to increase cost prediction accuracy and minimise estimation mistakes (Giang & Sui Pheng, 2011). The future of AI in the construction sector is positive, with the global construction business expected to increase significantly (Barbosa & Woetzel, 2017). This expansion is likely to be driven by technological breakthroughs across the building lifecycle, including planning, design, and construction. AI has the potential to enhance construction industry productivity by 0.8% to 1.4% per year, adding to the sector's overall development and efficiency (Regona et al., 2022). However, the building industry's delayed adoption of new technologies has hampered growth, emphasising the need for more study and deployment of AI solutions. Another critical part of building project management is risk assessment. Risk identification, evaluation, and management are critical for avoiding cost overruns and ensuring project success (Afzal et al., 2019). Traditional risk assessment approaches frequently fail to reflect the complex and interdependent nature of hazards in construction projects, especially when there is a lot of scepticism (Abdelgawad & Fayek, 2010). AI-based approaches provide novel solutions by merging various AI techniques such as fuzzy logic, neural networks, evolutionary algorithms, and object-oriented approaches to handle risk interdependencies and increase risk assessment accuracy.

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CHAPTER 2

LITERATURE REVIEW:

2.1 The Future of AI in Construction: Potential Growth and Technological Advancements

Over the past few decades, the use of hybrid systems based on artificial intelligence (AI) has gained popularity in the field of building risk management. These systems effectively combine various AI algorithms to address the complexities and interdependencies of construction projects, especially when facing significant uncertainty (Afzal et al., 2019). The global construction market is expected to experience significant growth, projected to increase by 85% by 2030, reaching a staggering USD 15.5 trillion. “AI technology stands at the forefront of playing an important part in the growth of the construction industry. Its potential lies in boosting productivity by approximately 0.8% to 1.4% annually, as highlighted by” (McKinsey ,2017, p4). This enhanced productivity is expected to be seen across various stages of the building lifecycle, including planning, design, and construction (Regona et al., 2022). However, despite the promising potential of AI, the building industry has been slow in adopting new technologies. In the past twenty years, the annual building output has shown minimal growth, with an average increase of less than 1%. This slow progress can be partly attributed to the construction sector's hesitation in adopting and fully incorporating innovative technologies like AI (Regona et al., 2022). Researchers have actively investigated and compiled existing "AI algorithms for cost-risk assessment in construction management" (Afzal et al., 2019, p1) to address this issue and harness AI's immense potential for construction risk management. “The objective is to develop sophisticated systems capable of capturing and resolving the complexity and interdependencies of hazards that frequently occur in building projects, particularly when significant degrees of uncertainty are present" (Alessandri et al., 2004). AI

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algorithms empower project managers and stakeholders with valuable insights for informed decision-making, proactive risk identification, and effective implementation of mitigation strategies (Afzal et al., 2019). The integration of AI in risk assessment and management holds immense promise for the construction industry, offering the potential to increase efficiency, save expenses, and minimise waste. “AI technology is advancing and gaining acceptance, poised to play a pivotal role in overcoming the complexities and uncertainties faced by construction projects. This will lead to improved productivity and performance across the sector” (Van Tam et al., 2021; Regona et al., 2022).

Fig 1. Construction technology map (McKinsey & Company, 2020) “The most prominent constellations include 3D printing, modularization, robotics, digital twin technology, AI, analytics, and supply chain optimization. “Three of the constellations digital twins, 3D printing, and AI and its analytics will be transformational

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for the industry” (Regona et al., 2022,p2) and marketplaces as seen in fig 1 (McKinsey & Company, 2020).

2.2 Improving Cost Prediction in Construction Projects: Challenges and Approaches

Cost prediction is an important activity for any corporation since it serves as a precursor over the project life cycle, for budget costs and resource allocation. When the scope of work is uncertain, it is difficult to obtain input data for the cost-estimating process, which may result in insufficient and imprecise estimates. Since more project needs are specified, the more explicit the project scope, the more likely it is to produce estimates that are more accurate. “it should be taken into account that, on the other hand, by the progressive elaboration, the process of cost control becomes more difficult if the project is based on inaccurate cost estimates” (Hashemi et al, 2020, p1; Schwalbe and Wahl, 2020; Cash, 2017). “Overestimation or underestimation of the expense of these activities will result in future budgetary issues. Cost discrepancies between expected and actual expenses” (Hashemi et al, 2020, p 1). As a result, there is a rising interest in the approaches utilized in this domain, as well as their respective accuracy and gaps. Methods that produce more consistent findings can help cost estimators by bridging gaps that must be studied and solved to achieve better outcomes. Traditional methodologies may be used to forecast total project costs by knowing work items, their price, and how they are distributed throughout the course of the project. This performs the task of allocating project resources and computing the subsequent budget. However, traditional methods have proven inadequate in this scenario. The lack of a systematic technique to decrease estimation error has led in research that has largely sought to overcome poor or even erroneous predictions using mathematical models, machine learning approaches, and so on. The estimated building costs of the project differs from the price in the tender because in that the tender price includes additional amounts such as profits for the company and emergency reserves. The emergency reserve is an expected quantity of reserve that is attributed to known risks during project execution (Spalzzi, 2001).

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Fig 2 Bid structure and analysis in projects (Lu et al., 2015; Hashemi et al, 2020) As seen in Fig. 2, the project cost comprises both indirect and direct expenditures. The direct cost of the project consists of expenditures directly spent on the project as well as secondary costs incurred either in the project or by the employees.

2.3 Enhancing Construction Management for Improved Productivity and Economic Growth

“The field of construction engineering and management (CEM) in the AEC business is full of difficulties and complications” (Cheng et al., 2021; Pan and Zhang, 2021, p1). Construction-related activities, processes, and human aspects are all included (Jin et al., 2019). The construction business, which contributes significantly to global GDP, is vital to promoting economic growth and national development (Giang and Sui Pheng, 2011). As a large industry, construction makes significant contributions to both economic growth and long-term national development (Giang and Sui Pheng, 2011; Free Essays PhDessay.com, 2017). “According to a McKinsey Global Institute survey conducted in 2017, the global construction industry accounts for roughly 13% of global GDP, with that amount projected to rise to 15% by 2020”(Manzoor et al., 2021,p1). In the global workforce,

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construction projects hold a significant place by employing 7% of workers while offering a wide range of career opportunities. Regardless of its economic importance, low labour productivity throughout the construction process is a major problem, resulting in a waste of employees, material resources, and cash. Due to the contribution of building operations greatly to our society's economic well-being, implementing adequate construction management to increase product performance makes the most sense. Raising the output in construction productivity by 50% to 60% or more is estimated to provide $1.6 trillion annually to the industry's value, significantly boosting global GDP. (Barbosa and Woetzel, 2017). The construction industry plays a crucial role in driving economic growth and national development as it contributes significantly to the global GDP. Its impact is far-reaching, making it an essential sector for fostering progress and prosperity (Giang and Sui Pheng, 2011). However, one important cause of worry is low labour productivity in construction, which leads to inefficiencies and resource waste (Barbosa and Woetzel, 2017). Improving construction management practises can boost productivity and considerably contribute to global GDP growth.

2.4 Ensuring Safety in the Construction Industry: Importance of Suitable Construction Management Practices and Architectural Design

Another significant concern in the building industry is safety. Construction sites are prone to accidents and fatalities because of a variety of uncontrollable elements such as participant positions, changing conditions, and equipment strikes (Sacks, Rozenfeld, and Rosenfeld, 2009; Wang and Razavi, 2018). Construction has a higher risk of fatal accidents than other industries, accounting for a significant number of deaths worldwide (Zhou, Goh, and Li, 2015; Zhang et al., 2013; Winge and Albrechtsen, 2018; Jo et al., 2017; Zhang et al., 2016). Several studies have stressed the significance of suitable construction management practices in ensuring

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safety and avoiding accidents (Li et al., 2018). Construction is considered one of the most dangerous industries due to several unpredictable factors such as participants in various positions, a fluctuating environment with high uncertainty, struck-by-equipment hazards, and others (Sacks, Rozenfeld, and Rosenfeld, 2009; Wang and Razavi, 2018). As a result, the construction industry has fewer fatal accidents than other industries, accounting for 30-40% of all fatal accidents worldwide (Zhou, Goh, and Li, 2015) About 26,000 construction workers died between 1989 and 2013 in the United States and during the year (Zhang et al., 2013). In 2014, 782 fatal construction accidents occurred in Europe, with an average of 13 deaths per 100,000 workers (Winge and Albrechtsen, 2018). According to reports from the Korean Ministry of Employment and Labor spanning from 2012 to 2015, the construction trade in Korea claimed the highest mortality rate among all economic sectors (Jo et al., 2017). The Chinese sector is described as one of the most dangerous in the world, fatal accident rates exceed many industrialized countries and show no signs of declining (Zhang et al., 2016).

2.5 AI-Based Approaches for Risk Assessment and Cost-Risk Analysis in Construction Projects

Artificial intelligence (AI) technologies are increasingly being used to address the difficulties of cost overruns and risk management in building projects. In the construction business, AI-based hybrid systems incorporating diverse AI algorithms have been widely employed for risk detection, evaluation, and prioritisation (Cho and Chae, 2022; Abdelgawad and Fayek, 2012). To resolve ambiguity in risk data, the fuzzy set theory (FST) technique is frequently used in conjunction with AI technologies (Samantra et al., 2017). However, to successfully manage cost overrun concerns, a thorough risk assessment technique that can capture risk interdependencies under high uncertainty is required (Afzal et al, 2019;Khodakarami and Abdi, 2014). Several research has been conducted to determine risk interdependencies to control cost uncertainty in complicated projects. (Afzal et al, 2019;Demirkesen and Ozorhon ,2017) investigated main risk interdependencies and their impact on project results using

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structural equation modelling (SEM) (Fang and Marle,2012) proposed a risk assessment technique based on simulation to study the complexity and risk interdependencies of building projects.( Afzal et al, 2019;Qazi et al., 2016) The researchers utilized both “Monte Carlo simulation (MCS) and multi-criteria decision model (MCDM)” (Khodabakhshian, Puolitaival and Kestle, 2023,p4) methodologies to assess the interconnectedness of complexity and risk within intricate building projects. (Afzal et al, 2019) Various AI approaches, such as multilevel regression (SEM), MCDM, probabilistic models, fuzzy hierarchical models (FHM), and genetic algorithms (GA), have been used in costrisk assessment (Afzal et al, 2019;Ebrat and Ghodsi, 2014; Li et al., 2013; Cheng et al., 2003). Building development, bridge infrastructure, tunnels/subways, highways, pipelines, power production, transmission, and other construction projects have all used these methodologies (Flyvbjerg et al., 2004). Failure mode and effect analysis (FMEA) is a risk analysis technique that is frequently used (Nuchpho, Nansaarng and Pongpullponsak, 2014).However, AI-based solutions have been employed in building projects to overcome the limits of traditional risk analysis methodologies. AI, particularly machine learning (ML), has shown considerable promise for risk assessment in building projects. ML models are capable of learning patterns and correlations from past data to effectively anticipate and categorize risks. This enables them to identify potential dangers proactively. Deep neural networks are used in DL, a subset of ML, to process complicated and non-linear risk data (Zhang et al., 2019). Another AI tool, natural language processing (NLP), can analyse unstructured textual data to find and extract risk-related information.

2.6 Addressing Challenges and Unlocking the Potential of AI-based Risk Assessment for Cost Overrun Prevention in Construction Projects

Cost Estimation and Management (CEM) in the architectural, engineering, and construction (AEC) industry is a highly complex and challenging process that involves a wide range of activities, processes, and interactions. To achieve successful CEM, it is crucial to implement effective risk assessment and mitigation strategies. These measures

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serve to prevent any potential cost overruns and ultimately ensure the overall success (Jin et al., 2019). AI has the potential to revolutionize risk assessment and prevent cost overruns in building projects. However, implementing AI comes with challenges. One critical obstacle is the data availability and data quality as building projects involve various stakeholders who generate diverse data types. This diversity and abundance of data can lead to difficulties in consolidating and standardizing information, making it challenging to develop accurate and reliable models for assessing risk (Han, Jentzen and E, 2018). Moreover, meaningful risk assessment and prediction for construction projects are particularly challenging because of inherent complexity of such endeavours. Building projects involve numerous interconnected variables, making it difficult to isolate and assess individual risks in isolation. Additionally, hazards in construction projects are often dynamic, constantly changing throughout the project's lifecycle, further complicating risk assessment efforts. The interrelation of risks further adds to the complexity, as a change in one aspect of the project can have ripple effects on other elements (Jin et al., 2019). Despite the challenges faced, AI tools and techniques hold significant potential in enhancing risk assessment models within the construction industry. By leveraging AI, construction professionals can enhance their ability to predict and manage potential risks, leading to more informed decision-making and better cost-control measures. One way to maximize the benefits of artificial intelligence (AI) is by utilizing hybrid AI techniques. These techniques involve combining different methodologies and approaches within AI. This integration enables a more comprehensive and robust evaluation of risk, providing a holistic perspective on potential uncertainties in construction projects (Han, Jentzen and E, 2018). However, before AI-based risk assessment models can fully realize their potential in the construction business, several key aspects need to be addressed. Data availability and standardization remain critical challenges. Collaborative efforts are required to streamline data collection and sharing processes. Thorough understanding and modelling of the complexity of risk factors and their interdependencies is necessary to develop accurate and reliable risk assessment frameworks. “As AI technology advances and these issues are adequately solved, its incorporation into CEM practises is predicted

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to provide considerable benefits, resulting in more efficient and cost-effective construction project management” (Van Tam et al., 202; Jin et al., 2019).

2.7 Managing Complexity and Uncertainty in Construction Projects: Contingency Planning and Cost Control

“In the realm of construction projects, various studies have highlighted the challenges created by complexity and ambiguity” (Kovacic and Di Felice, 2019). In the realm of large-scale construction endeavours, various factors come into play that can pose challenges when attempting to accurately forecast costs. One primary source of uncertainty is the uncertain ground conditions, which may significantly effect the project's overall cost and timeline. The implementation of contingency plans has emerged as a key strategy for managing complexity and risk in such uncertain environments. Contingency plans are pre-defined measures and strategies put in place to handle unexpected events or risks that may arise during the project. These plans act as a safety net, helping project managers mitigate potential cost overruns and schedule delays caused by unforeseen circumstances. A critical aspect that exacerbates uncertainty in project cost estimation is that complexity and risk variables are interconnected in a network. In construction projects, various elements and variables are intricately interconnected, creating a complex web of relationships. The interaction among these factors can lead to unpredictability or chance occurrences in the behaviour of project costs. To effectively manage cost overruns in projects with high uncertainty, it is crucial to recognize the dynamic and highly interdependent nature of construction endeavours. This requires a proactive approach to risk management and cost control. Cost estimation methods that follow a linear approach might not be enough in certain situations. Instead, adopting more sophisticated and data-driven techniques, such as AI-powered risk assessment models, can provide deeper insights into potential risks and their interconnected effects on costs. Project managers must remain vigilant and adaptive, continually reassessing and updating contingency plans as the project progresses. Being prepared to handle

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unforeseen events and having alternative courses of action in place can make a significant difference in controlling cost overruns. In conclusion, complexity and uncertainty are inherent challenges in construction projects, especially when faced with uncertain ground conditions and interdependent factors. The successful management of risks and costs in construction projects relies on well-executed contingency plans. By utilizing advanced tools such as AI-powered risk assessments, project managers can effectively navigate uncertainties and maintain control over expenses. This proactive approach to cost management is crucial for achieving favorable outcomes amidst unpredictable environments.

2.8 Neutral network A neural network is an enormous processor deployed in parallel that is composed of fundamental neuronal processing units compute and store information. With the right network architecture and settings, NNs may be created to solve indistinct issues. Although, NNs for learning multiple functions have struggled to find an appropriate functional structure.

2.9 AI-Based Approaches for Risk and Uncertainty Analysis in Construction Projects: Hybrid Techniques, Risk Interdependencies, and Failure Mode and Effect Analysis (FMEA)

Diverse AI technologies have been utilised for risk and uncertainty analysis to address cost overrun concerns in construction over the years. “AI-based hybrid techniques, for example, are frequently utilised in the construction sector for risk identification, evaluation, and prioritising” (Afzal et al,2019; Abdelgawad and Fayek, 2012). Similarly, the FST methodology is commonly employed in conjunction with AI technologies to address the issue of ambiguity in risk data (Samantra et al., 2017). “However, it has been observed that for complex projects, a complete risk assessment approach is required in order to detect risk interdependencies under high uncertainty and manage cost overrun concerns” (Afzal et al., 2019; Khodakarami and Abdi, 2014). “Failure mode and effect analysis (FMEA) is one of the risk analysis techniques recommended by international standards such as MIL-STD-1629A (U.S.

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Department of Defense 1980)” (Abdelgawad and Fayek, 2010,p1). FMEA is a strategy for identifying probable failures, investigating underlying causes, and reviewing failure repercussions to prevent these impacts. “The degree of criticality of a failure mode is determined by calculating the risk priority number (RPN). The RPN ranges from 1 to 1,000 and is an index score calculated as the product of the severity (S), occurrence (O), and detection (D) of a failure mode” (Abdelgawad and Fayek, 2010,p1). In the realm of high RPN systems, the components that hold greater significance are often prioritized over those found in low RPN systems (Aized et al., 2020). The severity S grade is used to reflect the probable consequences of a failure mode occurrence. As a result, it represents the gravity of the failure's consequences. The occurrence grade is O which represents the frequency with which the failure occurs. According to (Ayyub,2003), “defined the detection rating (D) as “a measure of the capability of the current controls.” Within traditional FMEA, a numerical scale ranging from 1 and 10 is used to represent the universe of discourse for severity (S), occurrence (O), and detection (D)” (Abdelgawad and Fayek, 2010,p2). The RPN value is calculated using the values supplied to these terms (Yarmohammadian et al., 2018; Abdelgawad and Fayek, 2010; Cheng and Ko, 2003; Cheung, 2009).

2.10 AI-Based Approaches for Risk and Uncertainty Analysis in Construction Projects: Fuzzy Logic, Neural Networks, Genetic Algorithms, and ObjectOriented Approach

2.11 Fuzzy logic Zadeh invented fuzzy logic in the 1960s to express uncertain and inaccurate information. It gives approximate but useful representations for mathematical systems that are exceedingly complicated, ill-defined, or difficult to study. A universal fuzzy logic system (FLS) is made up of four basic parts: a fuzzifier, a rule base, an inference engine, and a demulsifier. Despite the benefits of FLS over traditional techniques, there are

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several limitations, including the setup of MF distributions, determining the composition operator, and gaining relevant fuzzy rules for use in applications. Despite this the FLS parameters may be built up using specialists' expertise, the difficulty in establishing such parameters grows with the problem's complexity. (Booch, 1982; Abdelgawad and Fayek, 2010; Ko and Cheng, 2003; Cheng and Ko, 2003; Cheng and Ko, 2006; Kinjo et al., 2004).

Defining the ideal network topology is a challenge due to its complexity. Furthermore, certain real-world applications have been limited by a lack of training procedures that consistently identify a globally optimum set of weights (Booch, 1982; Ko and Cheng, 2003; Cheng and ko,2006 Kinjo et al., 2004).

2.12 Genetic algorithms Holland introduced GAs in the 1970s to imitate certain recognised natural evolution processes. GAs has few mathematical prerequisites, for optimization problems. “Furthermore, the ergodicity of genetic and evolutionary operations makes GAs useful for global search Furthermore, because genetic and evolutionary operations are ergodic, GAs are ideal for global search” (Slowik and Kwasnicka, 2020). Furthermore, due to its simple implementation technique, the GA allows for tremendous flexibility in combining it with domain-specific Histograms are used to efficiently implement certain concerns. Because of these benefits, GAs have garnered a lot of attention in terms of their potential as a novel optimization tool. In many challenging optimization situations, directly applying simple genetic algorithms can be a difficult and unsuccessful endeavour (Booch, 1982; Ko and Cheng, 2003; Cheng and Ko, 2003; Cheng and Ko, 2006; Kinjo et al., 2004).

2.13 Object-oriented approach Objects are abstractions of physical and mental entities with relationships, properties, and behaviours. Object orientation is a notion that considers all physical and intellectual entities to be objects. A system, according to the OO technique of system development, is a collection of interacting objects that collaborate to complete tasks. Despite

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tremendous gains in processing power, software development remains difficult. The benefits of improved extensibility, maintainability, reusability, and configuration management of specs, design, and analysis support the OO approach as a commonly used software development paradigm(Booch, 1982).

Fig 3 Construction 4.0+AI techniques (Baduge et al., 2022) Fig. 3 depicts potential AI developments in the construction sector. Smart robots, Cloud Virtual Reality (VR)/Augmented Reality (AR), Artificial Intelligence of Things (AIoT), Digital Twins, 4D printing, and blockchain were named as the most promising AI-assisted technologies that would dominate the construction sector in the future (Baduge et al., 2022).

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2.14 Integration of Fuzzy Logic and Neural Networks for Intelligent Systems: Fuzzy Neural Networks (FNN) as a Unified Approach

“To imitate the high degree of the model is based on human thinking and is built on FL. FL and NNs are complementary technologies” (Cheng and Ko, 2003) .The integration of three technologies into a single system looks to be a potential approach toward the creation of intelligent systems capable of capturing characteristics of the human brain. In the replacement process, the NN takes over both the fuzzy inference engine and fuzzy rule base of the classic fuzzy logic system. The role of the inference engine and the rule base are represented by the architecture of NN and the recollection of neural processing, respectively. The FL-NN combination is known as a "neuro with fuzzy inputoutput," which is a neural network with both fuzzy inputs and fuzzy outputs. The fusion of fuzzy logic (FL) and neural network (NN), known as a Fuzzy Neural Network (FNN), is commonly referred to as "neuro with fuzzy input-output". This term encompasses the combination and integration of FL and NN techniques. Several studies have- explored this concept, including works by authors such as (Ko and Cheng,2003).

Fig 4 Components, types, and subfields of AI (Regona et al., 2022)

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Fig 4 shows the subsets of AI, types of AI, Robotics, Computer vision, knowledge based systems, and other components of AI.

2.15 AI-Powered Techniques for Reducing Construction Cost Overruns: Predictive Analytics, Computer Vision, Natural Language Processing, Virtual Reality, and Augmented Reality

One of the most notable benefits of AI-powered risk assessment systems is their ability to process enormous amounts of data quickly and properly, which may assist project managers in discovering hazards that would otherwise go undiscovered. Here are some examples of how artificial intelligence (AI) is being utilised to reduce construction cost overruns: Analytics Predictive: Predictive analytics driven by artificial intelligence are being used to examine prior project data to uncover patterns and trends that may indicate potential hazards and cost overruns. For example, Lendlease, a worldwide property and infrastructure corporation, is analysing data from prior projects with predictive analytics tools to detect elements that lead to cost overruns, such as weather conditions, material pricing, and personnel availability. Lendlease may use this data to identify possible cost overruns on future projects and take efforts to avoid such risks. Computer Vision: Artificial intelligence (AI)-powered computer vision systems are being used to monitor building sites in real-time and identify possible safety concerns and quality issues. Hilti, a construction technology firm, has created an AI-powered camera system that can monitor building sites and identify possible safety hazards, such as employees who are not wearing hard helmets or safety harnesses. Project managers may reduce delays and cost overruns by recognising possible safety concerns early in the process. Natural Language Processing: Artificial intelligence-powered natural language processing techniques are being used to analyse construction contracts and detect possible Problems with safety, insurance, and compliance. DocuSign's Contract Intelligence service, for example, analyses construction contracts using natural language processing algorithms to detect possible hazards relating to payment terms, indemnity provisions,

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and insurance needs. Project managers can effectively reduce cost overruns and mitigate risks by proactively identifying potential issues at an early stage. AI-powered virtual reality tools are being used to model building projects and detect possible dangers and difficulties before work begins. Mortenson, for example, employs AI-powered virtual reality technology to model building projects and detect possible safety and quality concerns. Project managers may reduce delays and cost overruns by recognising possible concerns early in the process. Augmented Reality: “Augmented reality technologies driven by artificial intelligence are being utilised to superimpose digital information on physical construction sites, providing project managers with real-time information on project progress and potential hazards” (Olutunde, 2017) Trimble's Site Vision solution, for example, overlays digital information onto building sites using AI-powered augmented reality technology, allowing project managers to monitor progress and identify possible concerns before they create delays and cost overruns.

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Fig 5 FMEA system (Abdelgawad and Fayek, 2010) Fig 5 explains the FMEA system for risk analysis.

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Fig.6 Frequency of papers from 1960 to 2020 (Abioye et al., 2021) The research on artificial intelligence in construction has seen a rise over the years ,as seen in fig 6 , which shows papers published on the topics such as “machine learning, deep learning, computer vision, natural language processing, knowledge-based reasoning, and robotics” (Benbya et al. 2020; Stone et al. 2016) and optimisation from the years 1960 to 2020

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2.16 summary of literature review

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Table 1, summary of literature review

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▪

CHAPTER 3

RESEARCH DESIGN AND METHODOLOGY

Fig 7 Research methodology (Elmousalami, 2020) The literature has identified several AI models that can be employed in construction to reduce cost overruns the data needs to be analysed based on past literature and AI to check which algorithum suits best to which type of construction type (fig 7)

3.1 Triangulation The research uses more than one method to collect data that is in the form of interviews, surveys and case studies. It has been put to use to validate the research and it will assist in improving the reliability and trustworthiness of the findings. (Fig 8)

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Fig 8 Triangulation Method and research workflow (Priatmoko et al., 2021)

3.2 Research Approach and Data Collection Methods for Assessing AI and Risk Assessment in Building Projects

A thorough study of the literature, a qualitative as well as quantitative online survey, statistical and covariance analyses, an overall analysis, and other methods as well as framework development model are all portions of the research process explained inside the paper. These procedures were chosen to collect information, analyse data, and create a conceptual framework for the usefulness and compatibility of deep learning coupled with procedural decision-making skills optimisation in building projects. As the first stage, a systematic literature review was done to acquire important information from existing studies and publications on the topic. This step contributes to a deep comprehension of the elements impacting building project risk in terms of cost overruns. Identifying construction firms that employ AI for their projects, as well as conducting interviews and surveys to understand the issues faced and the use of fuzzy hybrid techniques, were all part of the data collection process. There were also case studies of projects that effectively used AI to reduce cost overruns.

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The paper describes how to analyse survey and interview data using descriptive statistics and covariance analysis. These statistical studies aid in summarising and comprehending the links between various AI and risk assessment variables in building projects. Theme analysis was also performed to uncover common themes and patterns in the qualitative data gathered from interviews and focus groups. This investigation contributes to a better understanding of the difficulties and opportunities associated with AI and risk assessment.

3.3 AI Applications for Risk Assessment and Cost Overrun Prevention in Building Projects: Examples of Companies and Technologies

Several corporations, firms, offices, and organisations throughout the world use artificial intelligence (AI) in the field of risk assessment to minimise building cost overruns. Here are a couple of such examples: Oracle Construction and Engineering: Oracle Construction and Engineering analyses previous project data using AI-powered predictive analytics capabilities to detect possible hazards and cost overruns. Primavera P6 software from the firm incorporates AI-powered predictive analytics tools that enable project managers to anticipate possible cost overruns and take Proactive actions are taken to reduce such dangers. Sky catches: Sky catch monitors building sites in real-time using AI-powered computer vision technology to identify possible safety concerns and quality issues. The drone based method used by the business gathers high-resolution photographs of building sites, which are then analysed by AI algorithms to spot possible difficulties before they worsen. Construct Secure: Construct Secure analyses construction contracts and identifies possible hazards linked to safety, insurance, and compliance using AI-powered natural technologies for handling languages. Contractors are assigned a risk score by the

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company's Contractor Intelligence Rating (CIR) system based on their compliance with different safety and insurance standards. Autodesk utilizes AI-powered virtual reality technology to create detailed construction project models and identify potential risks and concerns, all before the physical building process begins. This advanced approach allows for proactive risk management and ensures a smooth construction process. AI-powered virtual reality elements in the company's BIM 360 software enable project managers to visualise construction projects and spot possible concerns early on. VIMaec: VIMaec overlays digital information onto actual construction sites using AIpowered augmented reality technology, giving project managers real-time information on project progress and potential hazards. In a neutral and objective tone, project managers have the ability to utilize the company's software. This allows them to effectively monitor progress and identify pote-ntial obstacles that may give rise to delays or cost overruns. Hilti: Hilti, a company utilizing AI-powered computer vision te-chnology, employs advanced monitoring technique-s on building sites. This innovative approach enables the identification of potential safe-ty concerns and quality issues. By deploying camera based technology, Hilti collects real time data from construction sites and swiftly analyzes it using AI algorithms to detect any possible problems.

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▪ CHAPTER 4 4

DATA COLLECTION

4.1 CASE STUDIES "The Oasia Downtown Hotel in Singapore is located at 100 Peck Seah Street in Singapore, Singapore."(fig 8) Dates for Projects in 2016 Design began on February 1, 2011, and construction began on February 1, 2012. It was completed in April of 2016. Estimated project cost of S$138 million Size of the Project 19416 sqm gross floor area Area of the plot: 2,311.4 sqm AMSL is 199.080 m. The number of storeys is 27 (World-Architects, 2016). The number of offices is 100. Hotel rooms: 314 Hotel Guestrooms." "224 Typical Hotel Rooms, 88 Hotel Club Rooms, 2 Suites" (World-Architects, 2016) Architects “WOHA Wong Mun Summ, Richard Hassell, Phua Hong Wei, Bernard Lee, Kim Young Beom, Evelyn Ng, Christina Ong, Huang Yue, Larissa Tan, Chen Shunann, Iyan Mulyadi, Oscar Korintus, Victoria Meadows, Simopoulou Olympia Konstantinou, Donovan Soon, Ang Chow Hwee, Dennis Kwek” (World-Architects, 2016; Scribd, n.d.).

The customer is Patricia Urquiola Interior Design Studio Far East SOHO Pte Ltd. Mechanical & Electrical Engineer, Rankine & Hill (S) Pte Ltd KTP Consultants Pte Ltd is a Singapore-based civil and structural engineering business. Rider Levett Bucknall is a quantity surveying business. Sitetectonix Pte Ltd is a landscape design firm. Irrigation consultant Christensen Irrigation (Singapore) Pte Ltd. Lighting Planners Associates (S) Pte Ltd provides lighting consulting services. Land Surveyor Huai Hoon (World-Architects, 2016)

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Principal ContractorWoh Hup (Private) Ltd, a prominent construction company, made significant strides in incorporating artificial intelligence (AI) into their design process to enhance energy efficiency and mitigate the risk of cost overruns due to energy consumption (World-Architects, 2016). They worked together with the esteemed architectural firm, WOHA Architects, to create advanced design tools that utilize artificial intelligence. The-se tools were de-veloped specifically for modelling and analysing the environmental impact of building de-signs. The focus was primarily on factors like wind flow and solar radiation. (World-Architects, 2016)

The Oasia Hotel Downtown, situated in Singapore's bustling Central Business District (CBD), became- renowned for its remarkable- utilization of AI technology. This distinctive skyscraper stood out from the typical sleek and sealed structures commonly found in temperate climates. Instead, it embodied the concept of "urban tropics land use intensification," earning the nickname of a "living tower" (World-Architects, 2016).

The implementation of AI-powered design tools allowed the architects and engineers to simulate and optimize various environmental factors related to the building's design. By accurately modelling wind flow patterns and solar radiation exposure, they could identify the most energy efficient configurations and materials. This approach had a dual benefit. Firstly, it contributed to constructing a more sustainable and eco friendly building. Se-condly, it significantly reduced the risk of cost ove-rruns associated with excessive- energy usage during the operating period.

AI was employed effectively by Woh Hup and WOHA Architects to anticipate and tackle potential energy related challenges at the- initial stages of the design process. By optimizing energy efficiency, they could ensure that the building would perform optimally in its tropical environment, mitigating the need for costly retrofits Perhaps more energy-intensive changes in the future.

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The Oasia Hotel Downtown (fig 9)served as an exemplary model for how AI-powered design tools could revolutionize the construction industry, particularly in urban environments and variable weather. This project demonstrated that technology, when thoughtfully integrated into the design and planning stages, could not only create visually stunning and innovative buildings but also achieve greater sustainability and Long-term cost-effectiveness. The collaboration between Woh Hup (Private) Ltd and WOHA Architects on the Oasia Hotel Downtown exemplifies the vast potential of AI in construction projects. By harnessing the power of AI to optimize energy efficiency and anticipate potential risks, they exemplified how technology could be leveraged to create a more sustainable and environmentally conscious future for urban architecture (World Architects, 2016).

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Fig 9 The Oasia Downtown Hotel (Oasiahotels.com ,2023)

The Crossrail Project in London employs AI-powered predictive analytics technologies to anticipate possible risks and cost overruns due to timetable delays and quality difficulties. Using these tools, project managers may take proactive steps to reduce risks 35

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and minimise cost overruns. The Crossrail maintenance strategy has been explained in fig 10.

Fig 10 The Crossrail maintenance strategy (Crossrail Learning Legacy, n.d.)

Western Sydney Airport, Australia: Before building begins, AI-powered virtual reality tools are used to model the construction process and identify possible hazards and concerns. Project managers may use these tools to make modifications to the project plan and minimise delays and cost overruns. Fig 11 gives a top view of the Western Sydney Airport.