Exploring Uncharted Architectural Territories through Generative Adversarial Networks with Human Collaboration (original) (raw)
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Architecture & Style | A New Frontier for AI in Architecture
Harvard University, 2019
We build here upon a previous piece, where our emphasis revolved around the strict organization of floor plans and their generation, using Artificial intelligence, and more specifically Generative Adversarial Neural Networks (GANs). As we refine our ability to generate floor plans, we raise the question of the bias intrinsic to our models and offer here to extend our study beyond the simple imperative of organization. We investigate architectural style learning, by training and tuning an array of models on specific styles: Baroque, Row House, Victorian Suburban House, & Manhattan Unit. Beyond the simple gimmick of each style, our study reveals the deeper meaning of stylistic: more than its mere cultural significance, style carries a fundamental set of functional rules that defines a clear mechanic of space and controls the internal organization of the plan. In this new article, we will try to evidence the profound impact of architectural style on the composition of floor plans. ------------------------------------------------------------------------------------------------------------------------------------ Article: https://towardsdatascience.com/architecture-style-ded3a2c3998f
AI + Architecture | Towards a New Approach
Harvard University, 2019
Artificial Intelligence, as a discipline, has already been permeating countless fields, bringing means and methods to previously unresolved challenges, across industries. The advent of AI in Architecture is still in its early days but offers promising results. More than a mere opportunity, such potential represents for us a major step ahead, about to reshape the architectural discipline. Our work proposes to evidence this promise when applied to the built environment. Specifically, we offer to apply AI to floor plans analysis and generation. Our ultimate goal is three-fold: (1) to generate floor plans i.e. optimize the generation of a large and highly diverse quantity of floor plan designs, (2) to qualify floor plans i.e. offer a proper classification methodology (3) to allow users to “browse” through generated design options. Our methodology follows two main intuitions (1) the creation of building plans is a non-trivial technical challenge, although encompassing standard optimization technics, and (2) the design of space is a sequential process, requiring successive design steps across different scales (urban scale, building scale, unit scale). Then, in order to harness these two realities, we have chosen nested Generative Adversarial Neural Networks or GANs. Such models enable us to capture more complexity across encountered floor plans and to break down the complexity by tackling problems through successive steps. Each step corresponding to a given model, specifically trained for this particular task, the process can eventually evidence the possible back and forth between humans and machines. Plans are indeed a high-dimensional problem, at the crossroad of quantifiable technics, and more qualitative properties. The study of architectural precedent remains too often a hazardous process, that negates the richness of the number of existing resources while lacking in analytical rigor. Our methodology, inspired by current Data Science methodologies, aims at qualifying floor plans. Through the creation of 6 metrics, we propose a framework that captures architecturally relevant parameters of floor plans. On one hand, Footprint Shape, Orientation, Thickness & Texture are three metrics capturing the essence of a given floor plan’s style. On the other hand, Program, Connectivity, and Circulation are meant to depict the essence of any floor plan organization. In a nutshell, the machine, once the extension of our pencil, can today be leveraged to map architectural knowledge, and trained to assist us in creating viable design options. Related Articles: • Background & Framework: https://medium.com/built-horizons/the-advent-of-architectural-ai-2fb6b6d0c0a8 • Organization: https://medium.com/built-horizons/ai-architecture-4c1ec34a42b8 • Style: https://medium.com/built-horizons/architecture-style-b7301e775488 Thesis PDF Online Viewer: https://view.publitas.com/harvard-university/ai-architecture-thesis-harvard-gsd-stanislas-chaillou/page/1
2023
Architecture, the built environment, and real estate have been joining the trend of artificial intelligence invading our lives and professions only belatedly. The record of some of the most recent "famous achievements" in the field set straight, the paper challenges the state-of-the-art concerning these fields, debunks the idea of (truly) creative potential of the technology, and puts forward a sketch roadmap to a realistic - and significant - deployment of artificial intelligence in architecture and the creation of the built environment. The attention turns to open-source patterns-platforms, generative patterns processing, generative pre-design, parametric evaluation and optimization. Finally, a chance for these disciplines to come back from the sidelines to the position they need to provide society with what it lacks in terms of quality of life, sustainability, and comprehensive resilience renders. Among other new technologies, artificial intelligence can play an outstanding role in this regard, if understood and developed adequately by architects and IT developers hand closely in hand.
2022
In recent years, generative models have been rapidly transforming into a broad field of research, and artificial intelligence (AI) works are increasing. Since deep learning technologies such as Generative Adversarial Networks (GANs) providing synthesized new images are becoming more accessible, researchers in the design and related fields are very much interested in adapting GANs into practice. Especially, StyleGAN has a strong capability for image learning, reconstruction simulation, and absorbing the pixel characteristics of images in the input dataset. StyleGAN also produces similar imitation outputs and summarizes all the input data into one "average output". The study aims to reveal the potential of these outputs that can be employed as a visual inspiration aid for designers. This article will discuss the outputs of the experiments, findings, and prospects of StyleGAN.
AI in architecture and engineering from misconceptions to game-changing prospects
Architectural Intelligence, 2024
Artificial intelligence invades our lives and professions at an ever-increasing pace and intensity. Architecture, engineering, construction, and operation of the real estate have been joining the trend only timidly and belatedly. The paper overviews the basic concepts, methods, general background, and results of artificial intelligence in architecture to date, discusses the achievements and prospects, and concludes the perspectives on the deployment of machine learning in the field. The record of some of the most recent "famous achievements" in the field is set straight and challenged, the flawed idea of a (truly) creative potential of the technology is debunked. Its roots equidistributed both in a farsighted vision of the next workflow of both productive and creative architectural and engineering designing, and construction and real estate management on the one hand and state-of-the-art machine learning on the other, an ambitious though realistic blueprint for R&D of AI-fostered architectural creativity, building design, planning, and operation is tabled for discussion. The attention turns to open-source patterns platforms, generative patterns processing, generative pre-design, parametric evaluation and optimization, latest achievements in machine learning building on reinforcement learning, imitation-based learning, learning a behavior policy from demonstration, and selflearning paradigms zooming in on the design-development processes instead of only on their results. Leveraging the objectivity of assessments and streamlining workflows, artificial intelligence promises to unleash true architectural creativity and leverage the productivity and efficiency of the design, planning, and operation processes.
Designing Tomorrow: AI and the Future of Architectural Design Process
Forum A+P, 2023
This essay explores the transformative role of Artificial Intelligence (AI) in the field of architecture, focusing on the impact on design innovation, production processes, as well as the ethical implications arising from inherent biases within these technologies. As AI becomes increasingly integrated into architectural practices, it offers the potential to revolutionize the discipline by enhancing efficiency, creativity, and sustainability. This investigation delves into the history and evolution of AI in architecture, tracing their journey from early computational design experiments to their current applications in generative design and robotic construction, which exemplify the shift towards more innovative and sustainable architectural practices. Furthermore, the essay highlights the involvement of the INNEN research team in integrating AI into academic and research activities within and besides the curriculum. It delves into the ways in which AI technologies are reshaping the boundaries of architectural design and construction, emphasizing on practical applications of AI in architectural design in education and professional work. This analysis uncovers the profound possibilities of AI in formulating groundbreaking design approaches and construction methods, underscoring research's role in propelling architectural thinking and practice forward through the use of technology.
Proceedings of S.Arch 2020, the 7th international conference on architecture and built environment, 2020
The proliferation of data together with the increase of computing power in the last decade has triggered a new interest in artificial intelligence methods. Machine learning and in particular deep learning techniques, inspired by the topological structure of neurons network in brains, are omnipresent in the IT discourse, and generated new enthusiasms and fears in our society. These methods have already shown great effectiveness in fields far from architecture and have long been exploited in software that we use every day. Many computing libraries are available for anyone with some programming skills and allow them to "train" a neural network based on several types of data. The world of architecture has not remained external to this phenomenon: many researchers are working on the applications of artificial intelligence to architectural design, a few design software allow exploiting machine learning algorithms, and some large architectural firms have begun to experiment with deep learning methods to put into practice data accumulated over years of profession, with a special interest in environmental sustainability and building performance. If on the one hand, these techniques promise great results, on the other we are still in an exploratory phase. It is then necessary, in our opinion, to understand what the roles of this technology could be within the architectural design process, and with which scopes they can facilitate such a complex profession as that of the architect. On this subject we made ten interviews with as many designers and researchers in the AEC industry, In the article we will report a summary of their testimonies, comparing and commenting on the responses of the designers, with the aim of understanding the potentials of using artificial intelligence methods within the design process, report their perceptions on how artificial intelligence techniques can affect the architect's approach to the project, concluding with some reflections on the critical issues identified during the interviews with the designers.
GAN as a generative architectural plan layout tool: A case study for training DCGAN with Palladian Plans and evaluation of DCGAN outputs Üretken Mimari Plan Aracı Olarak GAN: Palladian Planları ile DCGAN Eğitimi ve DCGAN Çıktılarının Değerlendirilmesi için Bir Durum Çalışması, 2020
GAN as a generative architectural plan layout tool: A case study for training DCGAN with Palladian Plans and evaluation of DCGAN outputs REFERENCES Author(s) (2005). Ahmad, A. R., Basir, O. A., Hassanein, K., & Imam, M. H. (2004). Improved placement algorithm for layout optimization. In Proc. of the 2nd Int’l Industrial Engineering Conf.(IIEC’04). Boucher, B. (1998). Andrea Palladio: the architect in his time. Abbeville Press. Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096. Chaillou, S. (2019). AI & Architecture. Retrieved from https://towardsdatascience.com/ai-architecture-f9d78c6958e0 Dalgic, H. O., Bostanci, E., & Guzel, M. S. (2017). Genetic Algorithm Based Floor Planning System. arXiv preprint arXiv:1704.06016. Dinçer, A. E., Çağdaş, G., & Tong, H. (2014). Toplu Konutların Ön Tasarımı İçin Üretken Bir Bilgisayar Modeli. Megaron, 9(2). Donald, T. (1962). A Sumerian Plan In The John Rylands Library1. Journal of Semitic Studies, 7(2), 184-190. Duarte, J. P. (2005). A discursive grammar for customizing mass housing: the case of Siza's houses at Malagueira. Automation in Construction, 14(2), 265-275. Eastman, C. M. (1973). Automated space planning. Artificial intelligence, 4(1), 41-64. Foscari, A., Canal, B., & Façade, G. T. (2010). Andrea Palladio. Unbuilt Venice. Baden: Lars Muller Publishers. Generative adversarial network. (2019). Retrieved from https://en.wikipedia.org/wiki/Generative\_adversarial\_network Giaconi, G., Williams, K., & Palladio, A. (2003). The Villas of Palladio. Princeton Architectural. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. Grasl, T. (n.d.). GRAPE For Web - Shape grammar interpreter. Retrieved from http://grape.swap-zt.com/App/PalladianGrammar Grason, J. (1971, June). An approach to computerized space planning using graph theory. In Proceedings of the 8th Design automation workshop (pp. 170-178). ACM. Hemsoll, D. (2016). Palladian Design: The Good, the Bad and the Unexpected. Hillier, B., & Stonor, T. (2010). Space Syntax-Strategic Urban Design. City Planning Review, The City Planning Institute of Japan, 59(3), 285. Huang, W., & Zheng, H. (2018). Architectural drawings recognition and generation through machine learning. In Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture, Mexico City, Mexico. Koning, H., & Eizenberg, J. (1981). The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and planning B: planning and design, 8(3), 295-323. Krejcirik, M. (1969). Computer-aided plant layout. Computer-Aided Design, 2(1), 7-19. Levin, P. H. (1964). Use of graphs to decide the optimum layout of buildings. The Architects' Journal, 7, 809-815. Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., ... & Benjamin, D. (2017, May). Project Discover: An application of generative design for architectural space planning. In Proceedings of the Symposium on Simulation for Architecture and Urban Design (p. 7). Society for Computer Simulation International. Puppi, L. (1973). Andrea Palladio (Vol. 2). Milano: Electa. Puppi, L., Codato, P., Palladio, A., & Venchierutti, M. (2005). Andrea Palladio: introduzione alle architetture e al pensiero teorico. Arsenale. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. Ravenscroft, T. (2019). Wallgren Arkitekter and BOX Bygg create parametric tool that generates adaptive plans. Retrieved from https://www.dezeen.com/2019/06/27/adaptive-floor-plans-wallgren-arkitekter-box-bygg-parametric-tool/ Rojas, G. S., & Torres, J. F. (2006). Genetic algorithms for designing bank offices layouts. In Prosiding Third International Conference on Production Research–Americas’ Region. Rykwert, J., & Schezen, R. (1999). The palladian ideal. New York: Rizzoli. Stiny, G., & Mitchell, W. J. (1978). The palladian grammar. Environment and planning B: Planning and design, 5(1), 5-18. Weinzapfel, G., Johnson, T. E., & Perkins, J. (1971, June). IMAGE: an interactive computer system for multi-constrained spatial synthesis. In Proceedings of the 8th Design Automation Workshop (pp. 101-108). ACM. Wundram, M., Marton, P., & Pape, T. (1993). Andrea Palladio 1508-1580: Architect between the renaissance and baroque. Taschen,.
Architectural Drawings Recognition and Generation through Machine Learning
Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA), 2018
With the development of information technology, the ideas of programming and mass calculation were introduced into the design field, resulting in the growth of computer aided design. With the idea of designing by data, we began to manipulate data directly, and interpret data through design works. Machine Learning as a decision making tool has been widely used in many fields. It can be used to analyze large amounts of data and predict future changes. Generative Adversarial Network (GAN) is a model framework in machine learning. It's specially designed to learn and generate output data with similar or identical characteristics. Pix2pixHD is a modified version of GAN that learns image data in pairs and generates new images based on the input. The author applied pix2pixHD in recognizing and generating architectural drawings, marking rooms with different colors and then generating apartment plans through two convolutional neural networks. Next, in order to understand how these networks work, the author analyzed their framework, and provided an explanation of the three working principles of the networks, convolution layer, residual network layer and deconvolution layer. Lastly, in order to visualize the networks in architectural drawings, the author derived data from different layer and different training epochs, and visualized the findings as gray scale images. It was found that the features of the architectural plan drawings have been gradually learned and stored as parameters in the networks. As the networks get deeper and the training epoch increases, the features in the graph become more concise and clearer. This phenomenon may be inspiring in understanding the designing behavior of humans.
Computers and Creativity in Architecture
eCAADe proceedings
The main purpose of this teaching and research project is to define those principles capable of determining a possible approach to computer-adied design for architecture-not seen as as a mere tool but as a way of supporting decision-making. This project for training architects is based on two fundamental principles: the study of urban development, as regards historical and motivational aspects, and the study of building types [A] as regards architectural composition, considered as the organization of empty spaces connected by constructing enclosures hierarchically, whose interrelations create an architectural language. These principles can be readily applied and are easily recognizable in buildings which have undergone some sort of planning and are reproducible. Using computers as an aid to decision-making in architectural design means it is necessary to configure an expert system endowed with Artificial Intelligence operating through a neural network. By means of a set of initial input and data this system must be able to provide the most suitable responses in architectural terms to the models proposed. Another approach is to check whether it is possible to synthesize new models. This first stage of research is particularly important