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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	caadria2022_297
id	caadria2022_297
authors	Zhou, Margaret Z., Chen, Shi Yu and Garcia del Castillo y Lopez, Jose Luis
year	2022
title	Elemental Motion in Spatial Interaction (EMSI): A Framework for Understanding Space through Movement and Computer Vision
doi	https://doi.org/10.52842/conf.caadria.2022.1.505
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. 505-514
summary	Spatial analysis and evaluation are becoming increasingly common as new technologies enable users, designers, and researchers to study spatial motion patterns without relying on manual notations for observations. While ideas related to motion and space have been studied in other fields such as industrial engineering, choreography, and computer science, the understanding of efficiency and quality in architectural spaces through motion has not been widely explored. This research applies techniques in computer vision to analyse human body motion in architectural spaces as a measure of experience and engagement. A taxonomy framework is proposed to categorize human motion components relevant to spatial interactions, for analysis through computer vision. A technical case study developed upon a machine-learning-aided model is used to test a selection of the proposed framework within domestic kitchen environments. This contribution adds further perspective to wider research explorations in the quality, inclusivity, engagement, and efficiency of architectural spaces through computer-aided tools.
keywords	Pose Estimation, Spatial Evaluation, Architectural Usability, Motion Studies, Computer Vision, SDG 3, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_195
id	caadria2022_195
authors	Li, Shuyang, Sun, Chengyu and Lin, Yinshan
year	2022
title	A Method of VR Enhanced POE for Wayfinding Efficiency in Mega Terminals of Airport
doi	https://doi.org/10.52842/conf.caadria.2022.1.079
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. 79-88
summary	The airport is one of the most essential infrastructures of cities. An important issue of the airport design is that passengers must be able to find their way efficiently. Although the designers adopt the post-evaluation after the operation, it takes a long time to conduct the on-site wayfinding experiment, and the number of participants of the experiment is also limited. Moreover, conventional post-occupancy evaluation suffers from security control and quarantine inspection that can not be carried out in the field. We proposed a VR enhanced POE approach that carries out an online wayfinding experiment to obtain numerous and detailed data, which significantly improves the efficiency of the post-occupancy evaluation project, and is validated by an affordable small-scale on-site experiment. Meanwhile, the cause for low wayfinding efficiencies, such as the symmetric space, the ambiguous direction and the redundant information on signboards are found and corresponding optimization suggestions are presented. The following signage system optimization project conducted in the terminal is welcomed by the passengers according to monthly questionnaires.
keywords	Transportation Building, Post-Occupancy Evaluation, Digital Twins, Signage System Design, Wayfinding, Virtual Reality, Eye-Tracking, SDG 9.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_431
id	caadria2022_431
authors	Sun, Ke Nan, Lo, Tian Tian, Guo, Xiangmin and Wu, Jinxuan
year	2022
title	Digital Construction of Bamboo Architecture Based on Multi-Technology Cooperation: Constructing a New Parameterized Digital Construction Workflow of Bamboo Architecture From Traditional Bamboo Construction Technology
doi	https://doi.org/10.52842/conf.caadria.2022.2.223
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. 223-232
summary	Limited by the non-standard nature of bamboo, bamboo has always been regarded as a traditional, restrictive, and time-consuming building material. Therefore, there is an urgent need for an enhanced parametric design system and digital construction workflow to upgrade the traditional bamboo construction process. In this paper, through the analysis of the bamboo pavilion "Diecui†Gallery" under the traditional construction method, five main factors restricting the development of bamboo architecture are obtained: difficult positioning of supporting structure, low efficiency of material selection and matching, the manual processing of materials, non-standard node and low utilization rate of non-standard waste materials. Then, through literature review, we proposed the technical means to improve these factors and put forward a multi-technology collaborative digital construction workflow. The workflow will comprise augmented reality, 3D scanning, robot-aided construction, 3D printing, and design rules. Moreover, by building parametric benches, we used augmented reality technology and new design rules to verify multi-technology collaborative fabrication workflow possibilities and effectiveness. This paper wants to explore a parametric design method based on bamboo material characteristics and multi-technology collaborative workflow, to improve the utilization rate of non-standard bamboo components in parametric design.
keywords	Bamboo Material, Multi-technology Collaboration, Parametric Design System, Augmented Reality, Digital Construction Method, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_393
id	caadria2022_393
authors	Yu, Daniel, Irger, Matthias, Tohidi, Alex and Haeusler, Matthias Hank
year	2022
title	Designing Out Heat ‚ Developing a Computer-Aided Street Layout Tool to Address Urban Heat in Existing Streets and Suburbs
doi	https://doi.org/10.52842/conf.caadria.2022.2.739
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. 739-748
summary	As cities are getting hotter, the urban heat islands effect will become an increased concern for cities. While urban heat migration strategies are well researched and understood, some strategies of implementing urban heat mitigation focus on private land - thus depend on the owner's uptake. This research shifts mitigation strategies to the public land where governments have legislative control over the corridor between privately owned cadastral ‚ the street corridor. This paper asks the question how a computational tool could assist councils in redesigning streets to mitigate urban heat. Literature review confirmed a direct relationship between the magnitude of urban heat and street layout, vegetation and materials used, position of street to sun and wind direction - yet no tool that assists a designer exists - the focus of the research. We present first findings and the iterative development of our street design tool. Via our tool one can alter variables such as vegetation type, materials or street configuration until urban heat mitigation is optimized. This is a significant step towards cooling our cities as designers now have a process that translates expert knowledge on urban heat into a tool that lets them design as well as evaluate their design.
keywords	Urban heat island, landscape architecture, urban design, traffic engineering, computational tools, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_167
id	caadria2022_167
authors	Aman, Jayedi, Matisziw, Timothy C, Kim, Jong Bum and Luo, Dan
year	2022
title	Sensing the City: Leveraging Geotagged Social Media Posts and Street View Imagery to Model Urban Streetscapes Using Deep Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.595
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. 595-604
summary	Understanding the relationships between individuals and the urban streetscape is an essential component of sustainable city planning. However, analysis of these relationships involves accounting for a complex mix of human behaviour, perception, as well as geospatial context. In this context, a comprehensive framework for predicting preferred streetscape characteristics utilizing deep learning and geospatial techniques is proposed. Geotagged social media posts and street view imagery are employed to account for individual sentiment and geospatial context. Natural Language Processing (NLP) and computer vision (CV) are then used to infer sentiment and model the visual environment within which individuals make posts to social media. An application of the developed framework is provided using Instagram posts and Google Street View imagery of the urban environment. A spatial analysis is conducted to assess the extent to which urban attributes correlate with the sentiment of social media postings. The results shed light on sustainable streetscape planning by focusing on the relationship between users and the built environment in a complex urban setting. Finally, limitations of the developed methodology as well as future directions are discussed.
keywords	Urban sustainability, data mining, pedestrian sentiments, transportation behavior, street level imagery, transformers, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_85
id	caadria2022_85
authors	Reinhardt, Dagmar, Holloway, Leona, Silveira, Sue and Larkin, Nicole
year	2022
title	Tactile Oceans - Enabling Inclusive Access to Ocean Pools for Blind and Low Vision Communities
doi	https://doi.org/10.52842/conf.caadria.2022.2.709
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. 709-718
summary	This research explores implementing computation to enhance access to ocean pool and marine landscapes for the inclusion of people who are blind or have low vision (BLV). Constructing reliable representations, explanations and descriptions can support interactions with objects and participation in activities, particularly in these ocean environments. We discuss the adoption of a series of computational design strategies to leverage the impact of recent scanning technologies in information transfer. The paper introduces a background to touch access and universal design. It presents a case study of aerial photogrammetry for an ocean pool in NSW, Australia, and presents multi-scalar workflows and processes across computational design and advanced fabrication methods, including a) photogrammetry through drone-flight on a macro-scale and 3D-scanning to establish data-sets; b) parametric design and scale adaptations;†and c) 3D printing and robotic milling for touch access.
keywords	Blind, Universal Design, Touch Access, Photogrammetry, 3D Printing, SDG 3, SDG 10, SDG 14
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_215
id	caadria2022_215
authors	Settimi, Andrea, Vestartas, Petras, Gamerro, Julien and Weinand, Yves
year	2022
title	Cockroach: an Open-source Tool for Point Cloud Processing in CAD
doi	https://doi.org/10.52842/conf.caadria.2022.2.325
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. 325-334
summary	In the architecture, engineering and construction (AEC) sector, the use of point cloud data is not a novelty. Usually employed to retrieve data for inspecting construction sites or retrofitting pre-existing buildings, sensors like LiDAR cameras have been known to practitioners such as architects and engineers for a while now. In recent years, the growing interest in 3D data acquisition for autonomous vehicles, robotic and extended reality (XR) applications has brought to the market new compact, performant, and more accessible hardware leveraging different technologies able to provide low-cost sensing systems. Nevertheless, point clouds obtained from such sensors must be processed to extract valuable data for any design or fabrication application. Unfortunately, most advanced point cloud processing tools are written in low-level languages and are hardly accessible to the average designer or maker. Therefore, we present Cockroach: a link between computer-aided design (CAD) modeling software and low-level point cloud processing libraries. The main objective is an adaptation to C# .NET via Grasshopper visual scripting interface and C++ single-line commands in native Rhinoceros workspaces. Cockroach has proved to be a handy design tool in integrating building components with unpredictable geometries such as raw wood or mineral scraps into new design and industrial fabrication processes.
keywords	Computer-vision, Point-clouds, Data-processing, 3D modeling, CAD interface, Open-source tools, Quality education, Industry innovation and infrastructure, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_114
id	caadria2022_114
authors	Dong, Zhiyong, Lin, Jinru, Wang, Siqi, Xu, Yijia, Xu, Jiaqi and Liu, Xiao
year	2022
title	Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.213
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. 213-222
summary	Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
keywords	Romance Heatmap, Generative Adversarial Networks, Deep Learning, Big Data Analysis, Correlation Analysis, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_272
id	caadria2022_272
authors	Dong, Zhiyong
year	2022
title	Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.263
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. 263-272
summary	City develops gradually with the lapse of time. Cities, as a ‚container‚, are injected new urban elements along the trajectory of the times and the progress of human civilization, constructing the historical structures involved past, present and future. Thus, the cultural information of each era is preserved in the urban fabric together and urban fabric features are complex and rich, which are difficult to capture in traditional design methods. In this paper, we try to use Generative Adversarial Networks (GAN), one of the neural network algorithms, to explore the inner rules of complex urban morphological features and realize the perception of the urban fabric. Neural networks are innovatively applied to the larger and more complex city generation in this experiment. First, we collect European urban fabric as the dataset, then label data to facilitate machine training, use GAN to learn the feature of the dataset by adjusting parameters, and analyze the effect of the generated results. The automatic feature learning capability of the neural networks is used to summarize the inherent patterns and rules in urban development which is difficult for human to discover.
keywords	Deep Learning, Generative Adversarial Networks, Generative Design, Morphology Cognition, Urban Fabric, SDG 11
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
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
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	caadria2022_93
id	caadria2022_93
authors	Feng, Jiajia, Liang, Yuebing, Hao, Qi, Xu, Ke and Qiu, Waishan
year	2022
title	POI Data Versus Land Use Data, Which Are Most Effective in Modelling Theft Crimes?
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
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. 425-434
summary	Alleviating crime and improving urban safety is important for sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI data. There is no agreement on which is more effective. To fill this gap, we systematically compare land use and POI data regarding their strength as well as the divergence and coherence in profiling urban functions for crime studies. Three categories of urban function features, namely the density, fraction, and diversity, are extracted from POI and land use data, respectively. Their global and local strength are compared using ordinary least square (OLS) regression and geographically weighted regression (GWR), with a case study of Beijing, China. The OLS results indicate that POI data generally outperforms land use data. The GWR models reveal that POI Density is superior to other indicators, especially in areas with concentrated commercial or public service facilities. Additionally, Land Use Fraction performs better for large-scale functional areas like green space and transportation hubs. This study provides important reference for city planners in selecting urban function indicators and modelling crimes.
keywords	POI, Land Use, Urban Functions, Theft crime, Predictive Power, SDG 16
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_357
id	caadria2022_357
authors	Bedarf, Patrick, Szabo, Anna, Zanini, Michele, Heusi, Alex and Dillenburger, Benjamin
year	2022
title	Robotic 3D Printing of Mineral Foam for a Lightweight Composite Concrete Slab
doi	https://doi.org/10.52842/conf.caadria.2022.2.061
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. 61-70
summary	This paper presents the design and fabrication of a lightweight composite concrete slab prototype using 3D printing (3DP) of mineral foams. Conventionally, concrete slabs are standardized monolithic elements that are responsible for a large share of used materials and dead weight in concrete framed buildings. Optimized slab designs require less material at the expense of increasing the formwork complexity, required labour, and costs. To address these challenges, foam 3D printing (F3DP) can be used in construction as demonstrated in previous studies for lightweight facade elements. The work in this paper expands this research and uses F3DP to fabricate the freeform stay-in-place formwork components for a material-efficient lightweight ribbed concrete slab with a footprint of 2 x 1.3 m. For this advancement in scale, the robotic fabrication and material processing setup is refined and computational design strategies for the generation of advanced toolpaths developed. The presented composite of hardened mineral foam and fibre-reinforced ultra-high-performance concrete shows how custom geometries can be efficiently fabricated for geometrically complex formwork. The prototype demonstrates that optimized slabs could save up to 72% of total concrete volume and 70% weight. The discussion of results and challenges in this study provides a valuable outlook on the viability of this novel fabrication technique to foster a sustainable and resourceful future construction culture.
keywords	robotic 3d-printing, mineral foam, stay-in-place formwork, concrete composite, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_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
doi	https://doi.org/10.52842/conf.caadria.2022.1.141
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
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

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