Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ascaad2021_022
id	ascaad2021_022
authors	Baºarir, Lale; Kutluhan Erol
year	2021
title	Briefing AI: From Architectural Design Brief Texts to Architectural Design Sketches
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 23-31
summary	The main focus of this research is to uncover the underlying intuitive knowledge of architecture with the help of machine learning models. To achieve this, a generic architectural design process is considered and divided into iterative portions based on their output for each phase. This study looks into the initial portion of the architectural design process called “Briefing”. The authors search for the intuition that exists within the design process and how it can be learned by artificial intelligence (AI) that is currently gained through master-apprentice relationship and experience that builds up this knowledge. In this study, a way to enable users to attain an architectural design sketch while defining an architectural design problem with text is explored. This on-going research decomposes the components of the briefing and preliminary design sketching processes. Therefore the domain knowledge at each phase is considered for translating to constraints via natural language processing (NLP) and machine learning (ML) models such as Generative Adversarial Networks (GANs).
series	ASCAAD
type	normal paper
email
last changed	2021/08/09 13:11

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2021_039
id	caadria2021_039
authors	Chen, Jielin, Stouffs, Rudi and Biljecki, Filip
year	2021
title	Hierarchical (multi-label) architectural image recognition and classification
doi	https://doi.org/10.52842/conf.caadria.2021.1.161
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 161-170
summary	The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website ArchdailyÂ®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style.
keywords	image recognition; hierarchical classification; multi-label classification; architectural functionality; style
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	cdrf2021_242
id	cdrf2021_242
authors	Waishan Qiu , Wenjing Li, Xun Liu, and Xiaokai Huang
year	2021
title	Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_23
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Recently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.
series	cdrf
last changed	2022/09/29 07:53

_id	ascaad2021_103
id	ascaad2021_103
authors	Yönder, Veli
year	2021
title	Case Studies of Incorporating BIM Models in the Digital Game Environment: Building Game Environment with BIM Tools and Game Scripts
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 507-520
summary	The emerging video game industry has provided opportunities for innovation and transformation starting with the late 20th century. In line with ever-changing needs and increasing demand, the extent of the digital gaming industry has outreached to the education sector and its subdomains besides the entertainment industry and its sub-branches as users obtain ambidextrous achievements through the gamification processes in which an experimental learning environ-ment is formed naturally. Numerous dissimilar disciplines from en-gineering, architecture, construction, work safety, renewable ener-gy, education, and health, etc. train users thru educational simula-tions prepared in digital environments to amplify their learning processes. Undoubtedly, the fields of architecture, engineering, and construction (AEC) are gradually adapting to the conditions of ac-celerating digitalization efforts in this era. Thus, BIM technology being one of the common denominators of the digitalization efforts in those fields serves the diverse agenda of the users with increas-ing popularity. Professional interaction and education may greatly benefit from conjoining the model outputs of BIM technology and interactive visual fidelity of the digital gaming industry. This ongo-ing research project aims to develop and compare two different BIM-based models of the historic Çardak Khan and the contempo-rary student center building by creating sophisticated digital game environments with architectural educational space-based informa-tive scenarios. Space-based virtual cards were created for each sce-ne. Research results in response to the diversity of spaces, geomet-ric qualities, number of scenarios and sequences were reported. Fur-thermore, textual data such as game scripts and drafts were ana-lysed with Voyant Tools.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2021_006
id	caadria2021_006
authors	Agirachman, Fauzan Alfi and Shinozaki, Michihiko
year	2021
title	VRDR - An Attempt to Evaluate BIM-based Design Studio Outcome Through Virtual Reality
doi	https://doi.org/10.52842/conf.caadria.2021.2.223
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 223-232
summary	During the COVID-19 pandemic situation, educational institutions were forced to conduct all academic activities in distance learning formats, including the architecture program. This act barred interaction between students and supervisors only through their computers screen. Therefore, in this study, we explored an opportunity to utilize virtual reality (VR) technology to help students understand and evaluate design outcomes from an architectural design studio course in a virtual environment setting. The design evaluation process is focused on building affordance and user accessibility aspect based on the design objectives that students must achieve. As a result, we developed a game-engine based VR system called VRDR for evaluating design studio outcomes modeled as Building Information Modeling (BIM) models.
keywords	virtual reality; building information modeling; building affordance; user accessibility; architectural education
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_146
id	ascaad2021_146
authors	Aly, Zeyad; Aly Ibrahim, Sherif Abdelmohsen
year	2021
title	Augmenting Passive Actuation of Hygromorphic Skins in Desert Climates: Learning from Thorny Devil Lizard Skins
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 264-278
summary	The exploitation of latent properties of natural materials such as wood in the passive actuation of adaptive building skins is of growing interest due to their added value as a low-cost and low-energy approach. The control of wood response behavior is typically conducted via physical experiments and numerical simulations that explore the impact of hygroscopic design parameters. Desert climates however suffer from water scarcity and high temperatures. Complementary mechanisms are needed to provide sufficient sources of water for effective hygroscopic operation. This paper aims to exploit such mechanisms, with specific focus on thorny devil lizard skins whose microstructure surface properties allow for maximum humidity absorption. We put forward that this process enhances hygroscopic-based passive actuation systems and their adaptation to both humidity and temperature in desert climates. Specific parameters and rules are deduced based on the lizard skin properties. Physical experiments are conducted to observe different actuation mechanisms. These mechanisms are recorded, and texture and bending morphologies are modeled for adaptive skins using Grasshopper.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
doi	https://doi.org/10.52842/conf.acadia.2021.160
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 160-169.
summary	This research aims to explore the quantitative relationship between urban planning decisions and the health status of residents. By modeling the Point of Interest (POI) data and the geographic distribution of health-related outcomes, the research explores the critical factors in urban planning that could influence the health status of residents. It also informs decision-making regarding a healthier built environment and opens up possibilities for other data-driven methods. The data source constitutes two data sets, the POI data from OpenStreetMap, and the PLACES: Local Data for Better Health dataset from CDC. After the data is collected and joined spatially, a machine learning method is used to select the most critical urban features in predicting the health outcomes of residents. Several machine learning models are trained and compared. With the chosen model, the prediction is evaluated on the test dataset and mapped geographically. The relations between factors are explored and interpreted. Finally, to understand the implications for urban design, the impact of modified POI data on the prediction of residents' health status is calculated and compared. This research proves the possibility of predicting resident's health from urban conditions with machine learning methods. The result verifies existing healthy urban design theories from a different perspective. This approach shows vast potential that data could in future assist decision-making to achieve a healthier built environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_038
id	caadria2021_038
authors	Chen, Jielin and Stouffs, Rudi
year	2021
title	From Exploration to Interpretation - Adopting Deep Representation Learning Models to Latent Space Interpretation of Architectural Design Alternatives
doi	https://doi.org/10.52842/conf.caadria.2021.1.131
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 131-140
summary	An informative interpretation of the hyper-dimensional design solution space can potentially enhance the cognitive capacity of designers with respect to both conventional design practice and the research domain of computational-aided generative design. However, the hitherto research of design space exploration has had limited focus on the interpretation of the hyper solution space per se due to the knowledge gap pertaining to representation and generation. Representation learning techniques, as a core paradigm in the statistically empowered domain of machine learning, possess the capability of extracting a convoluted probabilistic distribution of hyperspace with latent features from unorganized data sources in a generalized manner, which can be an intuitive modus operandi for a structural interpretation of the intricate latent design solution space and benefit the challenging task of architectural design exploration. We examine and demonstrate the potential capabilities of representation learning techniques for the interpretation of latent architectural design solution space with consideration of disentanglement and diversity.
keywords	Design space exploration; latent space interpretation; representation learning; deep generative modelling; generative architectural design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_415
id	caadria2021_415
authors	Chuang, Cheng-Lin and Chien, Sheng-Fen
year	2021
title	Facilitating Architect-Client Communication in the Pre-design Phase
doi	https://doi.org/10.52842/conf.caadria.2021.2.071
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 71-80
summary	The process of architects exploring the program with clients often take place through face-to-face oral discussions and visual aids, such as photos and sketches. Our research focuses on two communication mediums: language and sketch. We employ machine learning techniques to assist architects and clients to improve their communication and reduce misunderstandings. We have trained a Naive Bayesian Classifier machine, the language assistant (LA), to classify architectural vocabularies with associations to design requirements. In addition, we have trained a Generative Adversarial Network, the sketch assistant (SA), to generate photo quality images based on architects' sketches. The language assistant and sketch assistant combined can facilitate architect-client communication during the pre-design stage.
keywords	Architect-Client Communication; Pre-design; Architectural Programming; Machine Learning; Schematic Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2021_389
id	caadria2021_389
authors	del Campo, Matias
year	2021
title	Architecture,Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design
doi	https://doi.org/10.52842/conf.caadria.2021.1.211
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 211-220
summary	The motivation to explore Attentional Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate a design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of GraphCNN to serve as a successful design methodology for a complex architecture program. In the area of Neural Architecture, this technique allows to interrogate shape through language. An alternative design method that creates its own unique sensibility.
keywords	Artificial Intelligence; Machine Learning; Artificial Neural Networks; Semiotics; Design Methodology
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	sigradi2021_118
id	sigradi2021_118
authors	Henriques, Gonçalo Castro, Xavier, Pedro Maciel, Silva, Victor de Luca and Bispo, Luca Rédua
year	2021
title	Designing Learning Methods: Programming with Visual and Textual Language in Python
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 819–830
summary	At the fourth industrial revolution, programming is gaining relevance, and it promises to be a fundamental teaching subject as math, science, languages or the arts. Architects project more than buildings; they have developed innovative methods and are among the pioneers developing visual programming. However, after more than 10 years of use visual programming in architecture, despite its fast learning curve, it presents limitations to address complex problems. To overcome them, we propose associating the advantages of visual with textual languages in Python. The article reports the process to implement the discipline “Computation for Architecture in Python” at FAU-UFRJ. The methodology comprises the translation and adaptation of generic programming disciplines, and exercises, for architecture. The results are encouraging and demonstrate that students value learning programming. However, despite the participants' satisfaction with the discipline, they report difficulties in programming fundamentals, such as lists, loops and recursion.
keywords	Computational Design, Visual Programming, Textual Programming, Mixed Languages, Python
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 377-386
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	sigradi2021_200
id	sigradi2021_200
authors	Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat
year	2021
title	A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 191–202
summary	Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well.
keywords	Architectural design, machine learning, conceptual design, deep learning, artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ecaade2021_252
id	ecaade2021_252
authors	Kotov, Anatolii and Vukorep, Ilija
year	2021
title	Gridworld Architecture Testbed
doi	https://doi.org/10.52842/conf.ecaade.2021.1.037
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 37-44
summary	Over centuries architects have developed frameworks of representation of the built surroundings in diverse types of drawings or models. With the rise of digital techniques, virtual models slowly replace these representation techniques but are still far from replicating the real world's ambiguity and complexity. This paper wants to address the representational problems of architecture combined with architecture-related AI systems and missing standardized tests for such systems. For this, we suggest a standardized computational testbed that can serve for developing, testing and benchmarking design solutions for abstracted architectural problems with various AI approaches in a game-like environment.Furthermore, this paper will discuss architectural problems' subdivision into atomic subtasks solvable by specific AI systems. Ideally, there is a waste number of possible architectural subtasks that can be applied. The paper presents some examples of possible architectural game strategies that abstractly deal with concepts of walls and borders, zones and connections. Although this paper mentions different Reinforcement Learning techniques, it is not focusing on fine-tuning the AI algorithms. It aims to help achieve automation of specific design workflow phases, then in the longer term to optimize and propose alternative design solutions and improve the architectural community's overall work.
keywords	Gridworld Testbed; AI Aided Architecture; Benchmarking AI Algorithms
series	eCAADe
email
last changed	2022/06/07 07:51

_id	caadria2021_196
id	caadria2021_196
authors	Lu, Yueheng, Tian, Runjia, Li, Ao, Wang, Xiaoshi and Jose Luis, Garcia del Castillo Lopez
year	2021
title	CubiGraph5K - Organizational Graph Generation for Structured Architectural Floor Plan Dataset
doi	https://doi.org/10.52842/conf.caadria.2021.1.081
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 81-90
summary	In this paper, a novel synthetic workflow is presented for procedural generation of room relation graphs of floor plans from structured architectural datasets. Different from classical floor plan generation models, which are based on strong heuristics or low-level pixel operations, our method relies on parsing vectorized floor plans to generate their intended organizational graph for further graph-based deep learning. This research work presents the schema for the organizational graphs, describes the generation algorithms, and analyzes its time/space complexity. As a demonstration, a new dataset called CubiGraph5K is presented. This dataset is a collection of graph representations generated by the proposed algorithms, using the floor plans in the popular CubiCasa5K dataset as inputs. The aim of this contribution is to provide a matching dataset that could be used to train neural networks on enhanced floor plan parsing, analysis and generation in future research.
keywords	Graph Theory; Algorithm; Architecture Design Dataset; Organizational Graph
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	acadia21_48
id	acadia21_48
authors	Nahmad Vazquez, Alicia; Chen, Li
year	2021
title	Automated Generation of Custom Fit PPE Inserts
doi	https://doi.org/10.52842/conf.acadia.2021.048
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 48-57.
summary	This research presents a machine learning-based interactive design method for the creation of customized inserts that improve the fit of the PPE 3M 1863 and 3M 8833 respiratory face masks. These two models are the most commonly used by doctors and professionals during the recent covid19 pandemic. The proper fit of the mask is crucial for their performance. Characteristics and fit of current leading market brands were analyzed to develop a parametric design software workflow that results in a 3D printed insert customized to specific facial features and the mask that will be used. The insert provides a perfect fit for the respirator mask. Statistical face meshes were generated from an anthropometric database, and 3D facial scans and photos were taken from 200 doctors and nurses on an NHS trust hospital. The software workflow can start from either a 2D image of the face (picture) or a 3D mesh taken from a scanning device. The platform uses machine learning and a parametric design workflow based on key performance facial parameters to output the insert between the face and the 3M masks. It also generates the 3d printing file, which can be processed onsite at the hospital. The 2D image approach and the 3D scan approach initializing the system were digitally compared, and the resultant inserts were physically tested by 20 frontline personnel in an NHS trust hospital. Finally, we demonstrate the criticality of proper fit on masks for doctors and nurses and the versatility of our approach augmenting an already tested product through customized digital design and fabrication.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2021_035
id	ecaade2021_035
authors	Newton, David
year	2021
title	Visualizing Deep Learning Models for Urban Health Analysis
doi	https://doi.org/10.52842/conf.ecaade.2021.1.527
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 527-536
summary	As humanity has become increasingly urbanized, physical and mental health problems have increased significantly among urban populations with a combined cost of treating these diseases estimated to be in the trillions of dollars. In parallel to these developments, a growing body of research suggests that the design of the built environment has significant correlations with both physical and mental health outcomes. This research, however, has been limited in its ability to make use of large remote sensing datasets to identify specific design features at the neighborhood scale that correlate with health outcomes. The development of methods that can efficiently find such correlations from ubiquitous remote sensing datasets, such as satellite images, would therefore allow researchers a greater level of insight into how specific urban planning and design features might relate to health. This research contributes knowledge on a novel mixed method workflow to address this issue.
keywords	Deep Learning; Urban Planning; Health; Artificial Intelligence; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_id	sigradi2021_5
id	sigradi2021_5
authors	Ng, Provides, Fernandez, Alberto, Doria, David, Odaibat, Baha and Karastathi, Nikoletta
year	2021
title	AI In+form: Intelligence and Aggregation for Solar Designs in the Built Environment
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 203–215
summary	Designers are increasingly challenged by a constant change of context and the interaction of layers of data from a huge variety of sources, from natural-artificial to human-machine. This research aims at mapping the interrelations of energy problems, bio- and artificial intelligence, and human-machine interaction to reflect and rethink the future of solar design. This paper first discusses its theoretical approach that stands at the convergence of light-harvesting systems, their aggregation and intelligence. Afterwhich, this paper explores their translation into iterative processes between designer and artificial intelligences, which is defined as rule/agent-based and machine learning systems; in particular, the relationship between Cellular Automata, Genetic Algorithm, and Generative Adversarial Networks (GANs) is discussed. Finally, it introduces a design project - @R.E.Ar_ - showing the proposed combinatorial pipeline and some preliminary results.
keywords	artificial intelligence, bio-inspired, solar design, Aggregation, human-machine interaction
series	SIGraDi
email
last changed	2022/05/23 12:10

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