thesis

advanced architectural design studio 1

professors: professor andrew hayes

description: The overlap of influences between realms of history & modernity, interior & exterior conditions, private & public space and controlled & open environments significantly affects human interaction in contemporary culture. It follows that the evolving transformation in how we move and work must also impact the way we understand community and create place. This studio will engage the relationship between these aligned, but distinctly different influences in order to understand and consider cultural, historic and technological forces. Also the overlap and tension between economic and commercial networks, as well as physical and spatial systems that make up a thriving commercial or general aviation airport need to be considered as well.

Students will interrogate the relationship of, and tension between, personal environments versus commercial environments, as well as the various aeronautical and technological systems that influence the modern transportation experience that is aerial flight in the 21st century. One possible way to intellectually position this project is to consider the historical aspects of transportation systems and their influence upon the landscape of the Florida peninsula. Geographically speaking, the state of Florida is ideally positioned to nurture aviation, due to its proximity to the Gulf of Mexico, the Caribbean as well as central and South America. The historical land and sea based routes of commerce established during the period of roughly 1500 – 1900 has given way to air based routes of commerce and recreation of modern day. Florida is ideally positioned to capitalize on these evolving patterns of culture and commerce.

This rich history as it pertains to Florida begs for a Museum and the students will explore this building typology. They will survey airport precedents (historic and contemporary and general aviation and commercial type airports). The design program will also examine the needs of the passenger and private pilot community at the designated site as well as the manner of engagement with the site (control, affect and influence).

professors: professor jordan trachtenberg professor anson stuart professor anthony abbate

description: This advanced level design studio focuses on the development of advanced architectural and urban design at multiple scales. The studio is situated in a real-world community. During the design process students have the opportunity to investigate and conduct independent research to learn about issues that face communities at the local level and develop a vision for the built environment that supports a livable, sustainable urban future in a tropical/subtropical climate zone. While final products from collaborative efforts will be prepared among student teams, students will undertake individual research and design projects within project initiatives (students will be graded both individually and as a group).

topical design studio

professors: professor christian feneck

description: This topical research studio focuses on complex design methodologies involving the interaction of color, space, and visual perception in regards to architectural experience. The beginning of the semester will focus primarily on Albers’ Interaction of Color sequence in order to develop a sensitivity and understanding of color as a spatial tool. The second phase will delve into perspectival and perceptual explorations as a design method. The culmination of this process will be a sited architectural proposal created through design methods that focus on the perceptual and phenomenological qualities of the built environment. During the design process students have the opportunity to investigate and conduct independent research. While products from collaborative efforts will be prepared among student teams, students will undertake individual research and design projects within project initiatives (students will be graded both individually and as a group).

professors: professor jeffrey huber

description: Over the next 100 years nothing else will radically change the South Florida built environment more than climate change and rising seas. How we adapt to living on, with, and over water will become a key design question. Urbanism is fundamentally a question of housing, since two-thirds of most cities’ land area generally consists of housing. Vital cities are made from neighborhoods which sponsor diversity in housing types, living densities, and mixed-use configurations in compact organizations. Each housing type plays a niche role in neighborhood functioning and structuring the right typological mix (between randomness and a monoculture) significantly determines neighborhood quality. In essence a city’s success is tied to the quality of its housing stock. Therefore, it is important to study housing and take on housing design within your education.

Furthermore, considering the current efforts of developing adaptation strategies to sea level rise across our region, South Florida will be a laboratory for exploration of flood-adaptive urbanism (a salty urbanism) consisting primarily of housing. This semester and will further address the challenge of the architect’s role in placemaking and entrepreneurship (developer) through the design of urban infill housing that is proactive and projective towards new emergent lifestyles. The housing will be developed on a specific site (Everglades, Biscayne Bay, and Rockland Ridge), as well as be tested within a larger neighborhood plan to determine its ability to inform new development structures and code modification. The studio design objectives are threefold, 1) provide a flood-adaptive architecture that informs a new lifestyle arrangement for living with, on, and over water; and 2) develop urban mixed-use housing for varying income levels (luxury, market-rate, workforce and affordable), potentially including live-work units, as well as micro units. The studio will provide understanding of the typological principles and housing patterns that sponsor particular living environments and attendant lifestyles 100 years from now. Students will have to engage in a bevy of Issues related to energy production, food production, water harvesting, and waste management in an artful and decentralized way. Design approaches will focus on vocabularies of layered public, semi-public, and private spaces. The added component of future sea level rise and heat provides a robust and innovative platform for reimagining South Florida living and engagement with water and higher temperatures. Students will undertake individual research and design projects for specific sites within the region. The goal is to position students for design leadership in the built environment through cultivating capacities in design visioning, interdisciplinary thinking, and communication of complex issues to general and non-professional design audiences alike.

professors: prof. dr. shermeen yousif

description: Moving beyond the deterministic rule-based parametric design approaches, AI models are now capable of autonomously defining their own parameters from information present in their input datasets. Recent AI methods, like generative deep learning, are considered “Learning Systems” that learn directly from data without input rules and can offer “unexpected” solutions (Hassabis 2018). With the incorporation of AI models, a second generation of generative design systems is emerging, marking a shift in design processes, towards an unlimited exploration of the design space. Adopting this premise, the studio interrogates the development and experimentation of a new design framework where multiple interconnected artificial intelligence models are employed at every design task, and to address a specific architectural system in a connected framework. Within this human-machine collaborative mode, creativity is augmented, and exploration is expanded, allowing innovative design solutions to emerge. Importantly, adopting a bio-centric rather than an anthropocentric approach, the project brief is to design a high-rise habitat with ecological design principles, Inspiration will be derived from natural systems towards symbiosis between architecture and nature, to achieve an ecologically adaptive micro-system where humans and natural ecologies co-exist.

The studio will enable D10 students to learn state-of-the-art artificial intelligence strategies, such as generative adversarial networks (i.e., Pix2Pix, CycleGAN, StyleGAN2) in addition to neural language models (i.e., CLIP+VQGAN, DALL-E) coupled with advanced computational methods in a new design process where architecture manifests through design research and exploration of innovative approaches. Finishing this studio, students will be equipped with creative design approaches and methods to articulate solutions that address the environmental challenges of the 21st century. The studio is research-based, in an interactive lab environment, and knowledge will be communicated through a series of lectures and workshops on artificial intelligence and environmentally driven design, applied to one continuous project. At each phase, a workshop will be given, and AI models and algorithmic tools will be shared with the students.

professors: prof. dr. jean-martin caldieron

description: The main studio objective is to position students for design leadership in the built environment through the cultivation of capacities in design visioning, interdisciplinary and collaborative thinking, and communication of complex issues to general and nonprofessional design audiences. Four general learning objectives will structure the studio: + Introduce students to pressing socio-environmental conditions for which design has a unique capacity to deliver integrated solutions. This initiates the question of creative practice and the role of “critical practitioner” or instrumental thinking for upperdivision students. + Engage multiple decision-making domains through allied knowledge fields and multidisciplinary practices in the course of authoring design proposals. + Introduce research and/or case study components in the design of context to enhance design intelligence and resourcefulness. + Establish a professional practice culture in which information, arguments, and design proposals are intelligently visualized so that they may be usefully engaged by professional and lay audiences. + To make positive contributions to the architectural academy via high-quality work and educationally enriched skills.

5.1

5.2

ShipBreak_3 | 5.1 + 5.2 + 5.3 + 5.4 Nicole Grueser + Ian Fennimore + Shambil Khan D10, Professor Manos Vermisso

5.3

5.4

5.5

5.6

5.5 + 5.8 | Spatial Manifestation of Narrative Structure Miguel Martinez-Valdes D10, Christian Feneck

5.6 | H + H Pavilion Hexacronic Hyperbody Infrastructure Jerrica Arias D9, Professor Sharmeen Yousif

5.7 | Air and Space Museum Christopher Montoya + Steven Vernon + Mauro Gelfusa D9, Professor Andrew Hayes

5.9 | Lifestyle + Lavell Cudjo Jr. + Milagro Valeiro D8, Professor Jordan Trachtenberg 5.7

5.10

5.12

Housing as a Catalyst, Porosity Follows Connectivity | 5.10 + 5.11 Fabiana Villafane D9, Professor Anson Stuart

Lifestyle + | 5.12 Lavell Cudjo Jr. + Milagro Valeiro D9, Professor Jordan Trachtenberg

5.14

5.16 Temporal Flux | 5.14 + 5.15 Stephanie Guererro + Milagro Valeiro + Alekz Reyes D9, Professor Andrew Hayes

Disambiguation | 5.16 + 5.17 + 5.18 Christopher Montoya + David Fazio + Alistair Euliette D10, Professor Shermeen Yousif

5.17

5.19

5.20

5.21

creative assemblies | 5.19 Ian Fennimore + Nicole Grueser + Shambil Khan D10, Professor Manos Vermisso

myCOhabitat | 5.20 Matthew Craven + Yagmur Akyuz + Luisa Giffoni D10, Professor Shermeen Yousif

symb-oasis| 5.21 Yagmur Akyuz + Kristin Evely + Luisa Giffoni D9, Professor Andrew Hayes

Dubai 2050 + | 5.22 Stephanie Guerrero + Milagro Valeiro + Kristin Evely D10, Professor Shermeen Yousif

How lusciously resplendent our world is. Our senses gather and convey information of the world around us. Our perceptions of these senses coalesce to create our reality and shape our experiences having us exist in a perceptual world. Merleau-Ponty writes, “…the experience of perception is our presence at the moment when things, truths, values are constituted for us; that perception is a nascent logos; that is teaches us, outside of all dogmatism, the true conditions of objectivity itself; that it summons us to the tasks of knowledge and action.”1 Our perceived senses are the primary way we come to know the world around us; they construct our understanding. Vision, being chief among our senses in crafting our perceived world, dominates much of our interaction with it. Vision has the ability to carry us through vast cities and up mountains, notice minute details, and travel to places we will never touch to supply our minds with information to shape our reality.

But the immediacy of our vision is not always necessary. We can remember scenes from our past, viewing them in the mind’s eye, and can even picture completely imaginary spaces. We can also know the touch of a distant or forbidden object with our haptic memory triggered by vision. However, the distinction of experienced and fictive conditions is often blurred in our mind. This allows us to better place ourselves within them, allowing us to be enveloped in a familiar creation. The ambiguity of our visual memory is sharpened and made real by our mind.

In my effort to understand and depict this phenomenon, I have crafted a painting process derived from the method utilized in the Josef Albers Interaction of Color sequence, and in no small part influenced by my own architectural training. It is a reactive and experiential way of building a composition. It begins with a rough idea of a spatial condition existing somewhere between remembered and fictive. The beginning of this is drawn directly on a final panel utilizing architectural perspective conventions. The general areas are then layered with color to shape the first spatial definitions. All subsequent layers are designed and colors chosen in relation to what is present in the preceding layers. The design and discovery process is not hidden in a completed work; abandoned moments and altered decisions are left visible as a composition comes into focus. The process is part of the finished painting.

Owing to the extreme relativeness of color, a high degree of subtlety and nuance can be achieved in depicting a multitude of spatial conditions. Color allows for more tender transitions and crass aberrations to be illustrated. It has proven to be a fruitful visual tool to depict spatial

qualities beyond the purely formal. Sometimes an idea calls for the most gossamer of layers to caress a space, and sometimes a brutally sharp opaque mass is better suited. The complexities of color make this possible.

The final interaction of the perceived colors and layered perspectives guide our vision through the crafted spaces allowing our mind to occupy them. The translucent nature of the paint also facilitates a more united temporal understanding of space to the extent that view is allowed to pass through surfaces giving a glimpse into what conditions will be or have been.

As much of what we see can drift into the background of our attention, the work seeks to make evident the spectacle of our experiences and highlight the fundamental role perception plays in our engagement and understanding of the world. “We know not what is actually there, but what we perceive to be there.”2 Ultimately, my paintings explore the relationship vision holds in our understanding and creation of places.

1. Merleau-Ponty, Maurice, et al. The Primacy of Perception: And Other Essays on Phenomenological Psychology, the Philosophy of Art, History and Politics (Studies in Phenomenology and Existential Philosophy). 1st ed., Northwestern University Press, 1964.

60 deep learning-based surrogate modeling for performance-driven generative design systems

Figure 2. The workflow of the proposed DP framework, real-time daylight performance evaluation predictions using deep learning. The left side is the authors’ workflow, and the right side is the users’ workflow.

abstract

Within the context of recent research to augment the design process with artificial intelligence (AI), this work contributes by introducing a new method. The proposed method automates the design environmental performance evaluation by developing a deep learningbased surrogate model to inform the early design stages. The project is aimed at automating performative design aspects, enabling designers to focus on creative design space exploration while retrieving realtime predictions of environmental metrics of evolving design options in generative systems. This shift from a simulation-based to a predictionbased approach liberates designers from having to conduct simulation and optimization procedures and allows for their native design abilities to be augmented. When introduced into design systems, AI strategies can improve existing protocols, and enable attaining environmentally conscious designs and achieve UN Sustainable Development Goal 11.

Keywords. Deep Learning; Artificial Intelligence; Surrogate Modeling; Automating Building Performance Simulation; Generative Design Systems; UN Sustainable Development Goal 11.

introduction

[...] In this paper, we address the environmental performance of the enclosure system where we target daylight analysis with the use of a deep learning-based surrogate model. The proposed surrogate model was developed in three stages: (1) generative modeling and daylight simulation for data acquisition; (2) DL-model training for building the surrogate model; and (3) assessment and validation. In our previously published Deep-Performance framework (Yousif & Bolojan, 2021), the investigation was limited to single-space floor plans without interior walls, to test the initial prototype of the proposed method. In this paper, we present further development to our method by improving the surrogate model and conducting additional experiments to include more complex spatial configurations for the input datasets, as well as interior wall partitions and multi-room floor plan layouts. Our improved method shows promising results with regards to accurate predictions of daylight performance for complex floor plan designs with interior wall partitions and multi-space configurations.(Figure 1) background

It is speculated that AI is most likely to have the biggest influence on performance-based aspects, particularly in architectural practice and urban design, where data-driven approaches and performance-informed design are becoming increasingly important (Leach, 2021). Reviewing existing literature, performative AI has seen an exponential increase in architectural research addressing multiple building performance aspects. [...] In approximating building energy modeling, the work of (Singaravel et al.) uses a method of componentbased machine learning for mimicking BPS (2018). Papadopoulos et al. (2018) employ machine learning techniques combined with genetic algorithm-based optimization to offer energy use evaluations of building designs. Research is also expanding in using AI for automating daylight analysis (i.e., Ngarambe et al., 2020; Shaghaghian & Yan, 2019) Despite such progress in performative AI, these daylight-related approaches represent undergoing experimentation and do not yet offer validated methods for real-time daylight performance prediction in generative systems, which has motivated this project. [..] Surrogate models are prediction models that seek to approximate the output of simulation models as closely as possible and can offer compact and instantaneous performance information instead of simulation (Forrester et al., 2008). GANs are techniques for training a machine to perform complex tasks in a generative process measured against a set of training images (Goodfellow et al., 2016; Leach, 2021). [...]

research methods

As explained above, this study was aimed at developing a new framework that incorporates an accurate approximation method, a surrogate model for predicting daylight studies of design options in generative design protocols.

The methodology included experimentation with the surrogate model techniques, developing the model to be integrated into a performance-driven generative framework, prototyping the overall framework, application, and testing. In the development phase (authors’ framework), the prototype was formulated into three tasks, as illustrated on the left side of Figure 2. First, (1) dataset acquisition was pursued using a parametric system with daylight simulation integrated, (2) the DL-based model was trained for prediction of daylight performance, and (3) assessment and validation studies were conducted, comparing prediction with actual simulations. For the system users (designers), in the application phase, the system becomes a two-process workflow that consists of a generative process (with floor plan design options) and a real-time daylight performance prediction offered by our trained model, as shown in the right part of Figure 2. [...]

Figure 7. Three building design options are shown in the far-left part, and their two copies in the central and far-right parts with automatic prediction are presented in the daylight simulation meshes for each floor plan.

professor dr. shermeen yousif daniel bolojan

discussions

Our system, as presented, can predict daylighting with high accuracy. More significantly, this test-case application has shown promise in automating other environmental performance goals. Automatic prediction is possible with such a surrogate model, which is useful for decisionmaking at the early stages of design. The prediction model is to be injected into design generation, as depicted in Figure 7. It will allow designers to explore a wide range of design choices while assessing design performance in real-time throughout the inference phase. The significance of retrieving predicted daylight analysis is its impact on morphology and associated design decisions in design development. Automation of performative aspects accelerates and improves design decision-making, allowing for a faster feedback loop between design decision and environmental evaluation.

The proposed method is 600 times faster than the typical annual daylight simulation performed by HoneyBee® and required for the floor plans under consideration. Compared to the HoneyBee® daylight simulation results that took 3 minutes for each simulation run, our surrogate model was able to offer a comparable accuracy of 90%, taking less than 0.3 seconds for each prediction. In a generative design process, when exploring 6000 design options, it would take 3*6000 = 18000 minutes, equivalent to 12.5 days to retrieve daylight simulation results using HoneyBee® or Diva, in contrast to 0.3*6000 = 1800 seconds, equivalent to 30 minutes using the surrogate model. Besides saving computation time, our method offers a pre-trained model that can be used by designers into generative protocols for instant feedback on daylight performance. This way, any designer can have access to environmental performance of their designs.

Presented here is research that contributes to the transition from simulation to prediction-based performance evaluation, using surrogate modeling. An approach was developed to provide real-time daylight performance predictions of high fidelity to enhance generative design methods. The findings suggest that deep learning approaches might be used to automate additional building performance measures. The significance of this research is to enable systematic performancedriven design space navigation by injecting trained models into a design process driven by designers’ creative exploration. The ultimate objective is to enable environmentally efficient and affordable design of the built environment using data-driven approaches. This goal is aligned with SDG 11 to make cities and human settlements resilient and sustainable. In order to achieve sustainability, design processes should be performance-driven and involve environmental feedback of design options and enable identifying environmental consequences of design decisions.

For future work, in further developing the model, we aim to use labeling the floor plans according to program activities to achieve an accurate simulation for realistic floor plans with multiple-program activities. Also, the next step is to add parameters for window heights and shading devices (and their dimensions), encoding this information into the floorplan labels. In addition, more dynamic and angled floor plans will be pursued in future applications. Another future work involves facilitating optimization by filtering the optimum layout/s in terms of their daylight performance. The framework will also be improved to achieve an articulated and searchable design space when navigating through thousands of design options. Also, additional AI models will be required to sort out successful design alternatives with higher environmental performance. Further improvements will be focused on formulating a designer-friendly interface that will be assessed by lay architects in empirical studies. To achieve computation efficiency, a cloud-based interface integrating our method will be targeted. In addition, with transfer learning, developing generalizable models will be pursued.

Figure 4. A sample of 4 design options was evaluated against 5 daylight simulation metrics of sDA, DLA, UDLI100, UDLI100-2000, and UDLI2000_more.

conclusions + future work

description

This ongoing research looks at the development of Neural Networks capable of identifying relevant compositional features in samples representing Antoni Gaudi – Sagrada Familia and samples from nature. There are meaningful tectonics defined by Sagrada Familia that are constrained to a typical nave, symmetry, and seriality of the underlaying composition. The goal here is to deploy Deep Learning strategies in order to liberate the intricacy, novelty and structural properties found in tectonic detail. The developed strategies enable the intricated tectonic detail and structural properties to be played out on more complex compositions (e.g., free up the plan, needed for contemporary world), thereby augmenting design potency.

The goal here is not to transfer one domain’s style to another, but rather to transfer one domain’s underlying compositional characteristics to another domain. Similar to the way humans learn, by sorting and filtering irrelevant information, the neural network learns to filter and exclude less relevant compositional features while enhancing the relevant ones. A very common design practice is that a designer will learn, consciously or unconsciously semantic representation of one domain (e.g., nature, sails etc.). The learned representation is than later reinterpreted through a particular filter e.g., architectural style, architectural culture etc., and translated into a different domain (e.g., design, architecture etc.).