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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	architectural_intelligence2022_10
id	architectural_intelligence2022_10
authors	Xiao Liu & Yupeng Wu
year	2022
title	A review of advanced architectural glazing technologies for solar energy conversion and intelligent daylighting control
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00009-6
source	Architectural Intelligence Journal
summary	Efficient management of solar radiation through architectural glazing is a key strategy for achieving a comfortable indoor environment with minimum energy consumption. Conventional glazing consisting of a single or multiple glass pane(s) exhibits high visible light transmittance and solar heat gain coefficient, which can be a double-edged sword, i.e., it allows sufficient sunlight to enter the building interior space for passive heating and lighting; on the other hand, it can cause glare discomfort and large cooling energy consumption. Among the various advanced glazing technologies being developed, Building Integrated Photovoltaic (BIPV) glazing has a prominent position due to its ability to reduce cooling load and visual discomfort while simultaneously generating electricity from sunlight. Recent years have witnessed remarkable advances in low-concentration optics such as Dielectric based Compound Parabolic Concentrators (DiCPCs), with a growing interest in the development of Building Integrated Concentrating Photovoltaic (BICPV) glazing to improve light harvesting and electric power output. One of the challenges faced by traditional BIPV glazing systems is the lack of dynamic control over daylight and solar heat transmission to cope with variations in weather conditions and seasonal heating/cooling demands of buildings. A promising solution is to integrate an optically switchable smart material into a BIPV glazing system, which enables dynamic daylighting control in addition to solar power conversion. Thermotropic (TT) hydrogel materials such as poly(N-isopropylacrylamide) (PNIPAm) and Hydroxypropyl Cellulose (HPC) are potential candidates for hybrid BIPV smart glazing applications, due to their unique features such as high visible transparency (in the clear state), strong light-scattering capability (in the translucent state) and large solar energy modulation. This paper reviews various types of electricity-generating glazing technologies including BIPV glazing and BICPV glazing, as well as smart glazing technologies with a particular focus on TT hydrogel integrated glazing. The characteristics, benefits and limitations of hybrid BIPV smart glazing are also evaluated. Finally, the challenges and research opportunities in this emerging field are discussed.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	acadia22_524
id	acadia22_524
authors	Xiao, Jun; Liu, Yubo; Deng, Qiaoming
year	2022
title	High-Density Building Form Generation Considering Daylight Performance
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. 524-535.
summary	In this case, aiming for a high-density building designed with high-quality daylighting, this article develops a building form generation program for daylight performance optimization integrating Cellular Automata (CA) and Genetic Algorithm (GA), tools that can provide a global daylighting optimization through the balance of competition and concession of agents. The CA model provides randomness and structural restriction, while GA provide optimization and convergence by evaluating and selecting a series of CA models. The model applies designed CA rules on daylighting and a mathematic proxy model for daylight performance in GA fitness calculation.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_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_172
id	caadria2022_172
authors	Xiao, Yahan, Hotta, Akito, Fuji, Takaaki, Kikuzato, Naoto and Hotta, Kensuke
year	2022
title	Urban Scale 3 Dimensional CFD Approximation Based on Deep Learning A Quick Air Flow Prediction for Volume Study in Architecture Early Design Stage
doi	https://doi.org/10.52842/conf.caadria.2022.1.303
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. 303-312
summary	The CFD generated by an object and its surroundings is critical during architectural design. The most common method of CFD calculation is to discretize the spatial region into small cells to form a three-dimensional grid or grid point and then apply a suitable algorithm to solve the equation iteratively until the steady state, which usually takes a significant amount of time before it converges to the exact solution of the problem. Deep learning is a subset of a Machine Learning algorithm that uses multiple layers of neural networks to perform in processing data and computations on a large amount of data. This paper presents a deep learning model CNN architecture to provide a quick and approximated 3-dimensional solution for the CFD. Our network speeds up 45 times compared to the standard CFD solver. Moreover, our network is able to predict a CFD in which the wind inlet and outlet appear at the same surface of a wind tunnel.
keywords	Urban Microclimate, Machine Learning, 3D Unet, Residual Block, 3 Dimensional CFD Prediction, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_326
id	cdrf2022_326
authors	Zidong Liu, Yan Li, and Xiao Xiao
year	2022
title	Predicting the Vitality of Stores Along the Street Based on Business Type Sequence via Recurrent Neural Network
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_29
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	The rational planning of store types and locations to maximize street vitality is essential in real estate planning. Traditional business planning relies heavily on the subjective experience of developers. Currently, developers have access to low-resolution urban data to support their decision making, and researchers have done much image-based machine learning research from the scale of urban texture. However, there is still a lack of research on the functional layout with shop-level accuracy. This paper uses a sequence-based neural network (RNN) to explore the relationship between the sequence of store types along a street and its commercial vitality. Currently, the use of RNNs in the architectural and urban fields is very rare. We use customer review data of 80streets from O2O platforms to represent the store vitality degree. In the machine learning model, the input is the sequence of store types on the street, and the output is the corresponding sequence of business vitality indexes. After training and evaluation, the model was shown to have acceptable accuracy. We further combined this evaluation model with a genetic algorithm to develop a business planning optimization tool to maximize the overall street business value, thus guiding real estate business planning at a high resolution.
series	cdrf
email
last changed	2024/05/29 14:03

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