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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	sigradi2022_298
id	sigradi2022_298
authors	Perry, Isha N.; Xue, Zhouyi; Huang, Hui-Ling; Crispe, Nikita; Vegas, Gonzalo; Swarts, Matthew; Gomez Z., Paula
year	2022
title	Human Behavior Simulations to Determine Best Strategies for Reducing COVID-19 Risk in Schools
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 39–50
summary	The dynamics of COVID-19 spread have been studied from an epidemiological perspective, at city, country, and global scales (Rabajante, 2020, Ma, 2020, and Giuliani et al., 2020), although after two years of the pandemic we know that viruses spread mostly through built environments. This study is part of the Spatiotemporal Modeling of COVID-19 spread in buildings research (Gomez, Hadi, and Kemenova et al., 2020 and 2021), which proposes a multidimensional model that integrates spatial configurations, temporal use of spaces, and virus characteristics into one multidimensional model. This paper presents a specific branch of this model that analyzes the behavioral parameters, such as vaccination, masking, and mRNA booster rates, and compares them to reducing room occupancy. We focused on human behavior, specifically human interactions within six feet. We utilized the multipurpose simulation software, AnyLogic, to quantify individual exposure to the virus, in the high school building by Perkins and Will. The results show how the most effective solution, reducing the occupancy rates or redesigning layouts, being the most impractical one, is as effective as 80% of the population getting a third boost.
keywords	Spatiotemporal Modeling, Behavior Analytics, COVID-19 Spread, Agent-Based Simulation, COVID-19 Prevention
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	acadia23_v2_340
id	acadia23_v2_340
authors	Huang, Lee-Su; Spaw, Gregory
year	2023
title	Augmented Reality Assisted Robotic: Tube Bending
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 340-349.
summary	The intent of this research is to study potential improvements and optimizations in the context of robotic fabrication paired with Augmented Reality (AR), leveraging the technology in the fabrication of the individual part, as well as guiding the larger assembly process. AR applications within the Architecture, Engineering, and Construction (AEC) industry have seen constant research and development as designers, fabricators, and contractors seek methods to reduce errors, minimize waste, and optimize efficiency to lower costs (Chi, Kang, and Wang 2013). Recent advancements have made the technology very accessible and feasible for use in the field, as demonstrated by seminal projects such as the Steampunk Pavilion in Tallinn, Estonia (Jahn, Newnham, and Berg 2022). These types of projects typically improve manual craft processes. They often provide projective guidelines, and make possible complex geometries that would otherwise be painstakingly slow to complete and require decades of artisanal experience (Jahn et al. 2019). Building upon a previously developed robotic tube bending workflow, our research implements a custom AR interface to streamline the bending process for multiple, large, complex parts with many bends, providing a pre-visualization of the expected fabrication process for safety and part-verification purposes. We demonstrate the utility of this AR overlay in the part fabrication setting and in an inadvertent, human-robot, collaborative process when parts push the fabrication method past its limits. The AR technology is also used to facilitate the assembly process of a spatial installation exploring a unique aesthetic with subtle bends, loops, knots, bundles, and weaves utilizing a rigid tube material.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:12

_id	caadria2022_394
id	caadria2022_394
authors	Li, Yuanyuan, Huang, Chenyu, Zhang, Gengjia and Yao, Jiawei
year	2022
title	Machine Learning Modeling and Genetic Optimization of Adaptive Building Facade Towards the Light Environment
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 141-150
doi	https://doi.org/10.52842/conf.caadria.2022.1.141
summary	For adaptive facades, the dynamic integration of architectural and environmental information is essential but complex, especially for the performance of indoor light environments. This research proposes a new approach that combines computer-aided design methods and machine learning to enhance the efficiency of this process. The first step is to clarify the design factors of adaptive facade, exploring how parameterized typology models perform in simulation. Then interpretable machine learning is used to explain the contribution of adaptive facade parameters to light criteria (DLA, UDI, DGP) and build prediction models for light simulation. Finally, Wallacei X is used for multi-objective optimization, determines the optimal skin options under the corresponding light environment, and establishes the optimal operation model of the adaptive facades against changes in the light environment. This paper provides a reference for designers to decouple the influence of various factors of adaptive facades on the indoor light environment in the early design stage and carry out more efficient adaptive facades design driven by environmental performance.
keywords	Adaptive Facades, Light Environment, Machine learning, Light Simulation, Genetic Algorithm, SDG 3, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_239
id	caadria2022_239
authors	Huang, Chenyu, Zhang, Gengjia, Yin, Minggang and Yao, Jiawei
year	2022
title	Energy-driven Intelligent Generative Urban Design, Based on Deep Reinforcement Learning Method With a Nested Deep Q-R Network
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 233-242
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
summary	To attain "carbon neutrality," lowering urban energy use and increasing the use of renewable resources have become critical concerns for urban planning and architectural design. Traditional energy consumption evaluation tools have a high operational threshold, requiring specific parameter settings and cross-disciplinary knowledge of building physics. As a result, it is difficult for architects to manage energy issues through 'trial and error' in the design process. The purpose of this study is to develop an automated workflow capable of providing urban configurations that minimizing the energy use while maximizing rooftop photovoltaic power potential. Based on shape grammar, parametric meta models of three different urban forms were developed and batch simulated for its energy performance. Deep reinforcement learning (DRL) is introduced to find the optimal solution of the urban geometry. A neural network was created to fit a real-time mapping of urban form indicators to energy performance and was utilized to predict reward for the DRL process, namely a Deep R-Network, while nested within a Deep Q-Network. The workflow proposed in this paper promotes efficiency in optimizing the energy performance of solutions in the early stages of design, as well as facilitating a collaborative design process with human-machine interaction.
keywords	energy-driven urban design, intelligent generative design, rooftop photovoltaic power, deep reinforcement learning, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_169
id	ecaade2022_169
authors	Chen, Ting-Chia, Tsai, Tsung-Han, Huang, Ching-Wen and Wang, Shih-Yuan
year	2022
title	Compliant Mechanism Moulding via NiChrome Wire Sintering Method
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 281–290
doi	https://doi.org/10.52842/conf.ecaade.2022.1.281
summary	This research proposed a unique process for the rapid manufacturing of large-scale compliant mechanism components. Using the characteristics of the NiChrome wire sintering method, it aims to rapidly fabricate a large-scale compliant mechanism model at low cost. NiChrome wire sintering is a method in which NiChrome wire is wound into a target pattern and then placed in a hot-melt material (TPU powder) to be energized and moulded. The low cost, high degree of freedom and one-piece characteristic of this new method bring new possibilities for the manufacturing process of compliant mechanism components. This research applies a new fabrication method to reduce the production cost and manufacturing difficulty of large kinetic installations. In benefitting from the non-mechanical wear characteristics of compliant mechanisms, the service life of manufactured installations can be greatly prolonged as well. The new fabrication method demonstrates an efficient way to produce a large scale of kinetic structure and provides a toolkit for designers.
keywords	Nichrome Wire Sintering, Rapid Prototyping, Elastic Material, Digital Fabrication, Compliant Mechanism
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia23_v1_196
id	acadia23_v1_196
authors	Bao, Ding Wen; Yan, Xin; Min Xie, Yi
year	2023
title	Intelligent Form
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 196-201.
summary	InterLoop employs previously developed workflows that enable multi-planar robotic bending of metal tubes with high accuracy and repeatability (Huang and Spaw 2022). The scale and complexity is managed by employing augmented reality (AR) technology in two capacities, fabrication and assembly (Jahn et al. 2018; Jahn, Newnham, and Berg 2022). The AR display overlays part numbers, bending sequences, expected geometry, and robot movements in real time as the robot fabrication is occurring. For assembly purposes, part numbers, centerlines, and their expected positional relationships are projected via quick response (QR) codes spatially tracked by the Microsoft Hololens 2 (Microsoft 2019). This is crucial due to the length and self-similarity of complex multi-planar parts that make them difficult to distinguish and orient correctly. Leveraging augmented reality technology and robotic fabrication uncovers a novel material expression in tubular structures with bundles, knots, and interweaving (Figure 1).
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	acadia23_v1_180
id	acadia23_v1_180
authors	Huang, Lee-Su; Spaw, Gregory
year	2023
title	InterLoop
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 180-187.
summary	InterLoop employs previously developed workflows that enable multi-planar robotic bending of metal tubes with high accuracy and repeatability (Huang and Spaw 2022). The scale and complexity is managed by employing augmented reality (AR) technology in two capacities, fabrication and assembly (Jahn et al. 2018; Jahn, Newnham, and Berg 2022). The AR display overlays part numbers, bending sequences, expected geometry, and robot movements in real time as the robot fabrication is occurring. For assembly purposes, part numbers, centerlines, and their expected positional relationships are projected via quick response (QR) codes spatially tracked by the Microsoft Hololens 2 (Microsoft 2019). This is crucial due to the length and self-similarity of complex multi-planar parts that make them difficult to distinguish and orient correctly. Leveraging augmented reality technology and robotic fabrication uncovers a novel material expression in tubular structures with bundles, knots, and interweaving (Figure 1).
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	caadria2022_325
id	caadria2022_325
authors	Cui, Qinyu, Zhang, Shuyu and Huang, Yiting
year	2022
title	Retail Commercial Space Clustering Based on Post-carbon Era Context: A Case Study of Shanghai
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 515-524
doi	https://doi.org/10.52842/conf.caadria.2022.1.515
summary	In the post-carbon era, it has become a development and research trend on adjusting commercial locations to help achieve resource conservation by using big data. This paper uses multi-source urban data and machine learning to make reasonable evaluations and adjustments to commercial district planning. Many relevant factors are affecting urban commercial agglomeration, but how to select the appropriate ones among the many factors is a problem to be considered and studied, while there may be spatial differences in the strength of each influencing factor on commercial agglomeration. Therefore, this paper takes Shanghai, a city with a high economic and commercial development level in China, as an example and identifies the influencing factors through a literature review. Next, this paper uses the machine learning BORUTA algorithm of features selection to screen the influencing factors. It then uses multi-scale geographically weighted regression model (MGWR) to analyse the spatial heterogeneity of factors affecting retail spatial agglomeration. Finally, based on the background of the changing transportation modes and the unchanged social activities in the post-carbon era, the future spatial planning pattern of retail commercial space is discussed to provide particular suggestions for the future location adjustment of urban commerce.
keywords	Business District Hierarchy, Agglomeration Effect, Spatial Variability, Multi-scale Geographically Weighted Regression Model, Machine Learning, Big Data Analysis, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 67–74
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_399
id	ecaade2022_399
authors	Johanes, Mikhael and Huang, Jeffrey
year	2022
title	Deep Learning Spatial Signature - Inverted GANs for Isovist representation in architectural floorplan
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 621–629
doi	https://doi.org/10.52842/conf.ecaade.2022.2.621
summary	The advances of Generative Adversarial Networks (GANs) have provided a new experimental ground for creative architecture processes. However, the analytical potential of the latent representation of GANs is yet to be explored for architectural spatial analysis. Furthermore, most research on GANs for floorplan learning in architecture uses images as its main representation medium. This paper presents an experimental framework that uses one-dimensional periodic isovist samples and GANs inversion to recover its latent representation. Access to GANs’ latent space will open up a possibility for discriminative tasks such as classification and clustering analysis. The resulting latent representation will be investigated to discover its analytical capacity in extracting isovist spatial patterns from thousands of floorplans data. In this experiment, we hypothetically conclude that the spatial signature of the architectural floor plan could be derived from the degree of regularity of isovist samples in the latent space structure. The finding of this research will enable a new data-driven strategy to measure spatial quality using isovist and provide a new way for indexing architectural floorplan.
keywords	Machine Learning, Isovist, Latent Representation, GANs Inversion, Spatial Signature
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_203
id	ecaade2022_203
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Perspectival GAN - Architectural form-making through dimensional transformation
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 341–350
doi	https://doi.org/10.52842/conf.ecaade.2022.1.341
summary	With the ascendance of Generative Adversarial Networks (GAN), promising prospects have arisen from the abilities of machines to learn and recognize patterns in 2D datasets and generate new results as an inspirational tool in architectural design. Insofar as the majority of ML experiments in architecture are conducted with imagery based on readily available 2D data, architects and designers are faced with the challenge of transforming machine-generated images into 3D. On the other hand, GAN-generated images are found to be able to learn the 3D information out of 2D perspectival images. To facilitate such transformation from 2D and 3D data in the framework of deep learning in architecture, this paper explores making new architectural forms from flat GAN images by employing traditional tools of projective geometry. The experiments draw on Brook Taylor’s 19th- century theorem of inverse projection system for creating architectural form from perspectival information learned from GAN images of Swiss alpine architecture. The research develops a parametric tool that automates the dimensional transformation of 2D images into 3D architectural forms. This research identifies potential synergic interactions between traditional tools and techniques of architects and deep learning algorithms to achieve collective intelligence in designing and representing creative architecture forms between humans and machines.
keywords	Machine Learning, GAN, Architectural Form, Perspective Projection, Inverse Perspective, Digital Representation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_193
id	caadria2022_193
authors	Tsai, Tsung-Han, Chen, Ting-Chia, Huang, Ching-Wen, Lu, Yen-Cheng and Wang, Shih-Yuan
year	2022
title	S.n.o.w_Sintering TPU via Nichrome Wire
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 243-252
doi	https://doi.org/10.52842/conf.caadria.2022.2.243
summary	This paper introduces and investigates NiChrome wire sintering, a novel fabrication technique in the field of additive manufacturing. With a combination of differentiated material states and material properties, this research generates forms with different sintering strategies through computation and fabrication systems. Rather than creating objects through selectively depositing melted material in a predetermined path, layer-by-layer, this rapid prototyping methodology generates 2D or 3D spatial wireframes by weaving NiChrome wire and sintering thermoplastic polyurethane (TPU) onto it by utilizing the instantaneous high temperature of NiChrome wire after electrification. A series of experiments is presented utilizing a proportional integral derivative (PID) temperature control system in cooperation with thermal camera equipment to ensure consistent results under the same conditions. In addition, the project focuses not only on developing NiChrome wire sintering systems but also on the applicabilities of this technique by fabricating wireframe surfaces under different situations.
keywords	Nichrome Wire Sintering, Rapid Prototyping, Elastic Material, Digital Fabrication, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_293
id	caadria2022_293
authors	Li, Andre, Zhang, Hong, Cui, Weiwen and Huang, Jie
year	2022
title	Implementation of Point Cloud and BIM Technologies in a Construction Workflow: A Case Study of a Building Project in Yuecheng District, China
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 567-576
doi	https://doi.org/10.52842/conf.caadria.2022.2.567
summary	In recent years, there has been a surge of retrofitting and building projects in rural China, to elevate the living standards in local areas. However, with the conventional use of surveying and inspection instruments, the amount of construction errors account to substantial waste of materials, time and labour. The issue is magnified in the current context that emphasises on efficient utilisation of resources. The emergence of laser scanning and BIM technologies is evident with scanning equipment and software being more accessible. This paper explores the use of the two technologies, to be integrated into the a construction workflow. The research includes a self-conducted site survey, data collection, data processing and analyses. The processed point cloud data is extracted and compared to the as-designed BIM model, to analyse and assess the construction errors in various scales. The result displays a significant portion of the building being out of tolerance and its causes. A theoretical framework is proposed to integrate point cloud and BIM technologies, not only to document and assess the overall building dimensional accuracy, but also to minimise construction errors and waste, ensuring a responsible consumption and production of building materials.
keywords	BIM, laser scanning, point cloud, construction workflow, cast-in-situ concrete structure, tolerance compliance, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2024_186
id	caadria2024_186
authors	Huang, Jingfei and Tu, Han
year	2024
title	Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 395–404
doi	https://doi.org/10.52842/conf.caadria.2024.2.395
summary	The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.
keywords	Urban Sentiment, Affective Reaction, Social Media, Machine Learning, Urban Data, Image Segmentation.
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	acadia22_598
id	acadia22_598
authors	Shen, Yang-Ting; Wang, Mi-Chi; Huang, Lien-Kai; Gao, You-Min; Yen, Chia-Chin
year	2022
title	The Reproduction of Chinese Traditional Timber Structure
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 598-603.
summary	In Chinese traditional timber building, “Dou-gong” stands as one of the most distinctive features to present the Chinese structure style. However, the preservation and reproduction of Dou-gong face difficulties due to the withering craftsman issue. This paper proposes a method to digitize the structure into BIM (building information modeling) and reproduce it via robot-based fabrication. By modeling these Dou-gong components with BIM technologies, we can establish a geometrical and non-geometrical 3D database. Then we use Autodesk Fusion and Grasshopper to design the robotic fabrication information whose information is transferred from 3D database models. Based on the fabrication information, including work paths and tool parameters, the KUKA robotic arm with six axes can precisely mill the wood materials into Dou-gong components without any traditional craftsman’s processing.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	caadria2022_102
id	caadria2022_102
authors	Gardner, Nicole, Haeusler, Matthias Hank, Yu, Daniel, Barton, Jack, Dunn, Kate and Huang, Tracy
year	2022
title	Revisiting Shoei Yoh: Developing a Workflow for a Browser-Based 3D Model Environment to Create an Immersive Digital Archive
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 687-696
doi	https://doi.org/10.52842/conf.caadria.2022.1.687
summary	The digitisation of architecturally significant buildings and sites creates opportunities to innovate methods of analysis, interpretation, representation, and audience engagement. To illustrate this potential, but also examine the attendant challenges, this paper outlines a research project that has digitised archival assets and living buildings designed by the Japanese architect Shoei Yoh to create an immersive 3D Spatial Archive. It focuses particularly on the creation of a browser-based 3D environment using WebGL technology that connects to and displays a repository of digitised archival assets. This includes the use of 3D scan data of Yoh's Naiju Community Centre project to accurately model the 3D immersive environment and a Grasshopper / Rhino into the glTF. File format (graphics library Transmission Format) workflow to render Naiju‚s complex geometry and detailed outdoor scenery. The paper demonstrates how using the .glTF File, which is an open format specifically for transmitting processed and pre-calculated 3D models, can improve the processing efficiency of web-browser based 3D environments. Improving the stability and processing speed of 3D browser-based environments is significant to enhancing how audiences can connect with and experience culturally significant sites remotely. The digital recreation and repurposing of Naiju (which is currently unoccupied and in a state of disrepair) as an immersive archival exhibition space operates to simultaneously protect the real building from over visitation, but also raise awareness of its cultural significance to support preservation efforts. In so doing, the paper makes a further contribution to the developing field of digital cultural heritage.
keywords	Digital Cultural Heritage, Browser-based Modelling, glTF File, Architectural Visualisation, Shoei Yoh, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_456
id	caadria2022_456
authors	Gong, Pixin, Huang, Xiaoran, Huang, Chenyu and White, Marcus
year	2022
title	Quantifing the Imbalance of Spatial Distribution of Elderly Service with Muti-source Data
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 455-464
doi	https://doi.org/10.52842/conf.caadria.2022.1.455
summary	With the growing challenge of aging populations around the world, the study of the elderly service is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Previous research frameworks are very case-specific with limited evaluation indicators that cannot be extended to other scenarios and fields. Based on multi-source data and Geographic Information System (GIS), this paper quantifies and visualises the imbalance in the spatial distribution of elderly services in 218 neighbourhoods in Shijingshan District, Beijing, China. Mortality data were obtained, and the most contributing indicators to mortality were investigated by correlation analysis. Finally, mapping between other facility indicators to mortality rates was constructed using machine learning to further investigate the factors influencing the quality of elderly services at the community level. The conclusion shows that the functional density of transportation facilities, medical facilities, living services facilities, and the accessibility of elderly care facilities are most negatively correlated with mortality. The correlation conclusion is combined with a machine learning prediction model to provide future recommendations for the construction of unbalanced elderly neighbourhoods. This research offers a novel systematic method to study urban access to elderly services as well as a new perspective on improving social fairness.
keywords	elderly service facilities, multi-source data, machine learning, SDG 3, SDG 10, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_140
id	caadria2022_140
authors	Huang, Shuyi and Zheng, Hao
year	2022
title	Morphological Regeneration of the Industrial Waterfront Based on Machine Learning
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 475-484
doi	https://doi.org/10.52842/conf.caadria.2022.1.475
summary	The regeneration of the industrial waterfront is a global issue, and its significance lies in transforming the waterfront brownfield into an eco-friendly, hospitable, and vibrant urban space. However, the industrial waterfront naturally has comparatively unmanageable morphological features, including linear shape, irregular waterfront boundary, and separation with urban networks. Therefore, how to subdivide the vacant land and determine the land-use type for each subdivision becomes a challenging problem. Accordingly, this study proposes an application of machine learning models. It allows the generation of morphological elements of the vacant industrial waterfront by comparing the before-and-after scenarios of successful regeneration projects. The data collected from New York City is used as a showcase of this method.
keywords	machine learning, urban morphology, industrial waterfront regeneration, sustainable cities, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	acadia22_672
id	acadia22_672
authors	Johanes, Mikhael; Huang, Jeffrey
year	2022
title	Latent Isovist
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 672-683.
summary	This research leverages the development in deep learning research to develop an experimental framework for discovering machine-human interpretable spatial properties from latent isovist, a reduced dimensionality isovist representation obtained from generative adversarial networks (GANs). GAN latent space contains a wide range of semantically interpretable directions, potentially being used to quantify the spatial properties encoded in isovist representation.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 727-736
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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