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	ecaade2023_205
id	ecaade2023_205
authors	Meeran, Ahmed and Joyce, Sam
year	2023
title	Rethinking Airport Spatial Analysis and Design: A GAN based data driven approach using latent space exploration on aerial imagery for adaptive airport planning
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 501–510
doi	https://doi.org/10.52842/conf.ecaade.2023.2.501
summary	Airports require long term planning, balancing estimations of future demand against available airfield land and site constraints. This is becoming more critical with climate change and the transition to sustainable aviation fuelling infrastructure. This paper demonstrates a novel procedure using Satellite Imagery and Generative Learning to aid in the comparative analysis and early-stage airfield design. Our workflow uses a GAN trained on 2000 images of airports transforming them into a high-dimensional latent space capturing the typologies’ large-scale features. Using a process of projection and dimensional-reduction methods we can locate real-world airport images in the generative latent space and vice-versa. With this capability we can perform comparative “neighbour” analysis at scale based on spatial similarity of features like airfield configuration, and surrounding context. Using this low-dimensional 3D ‘airport designs space’ with meaningful markers provided by existing airports allows for ‘what if’ modelling, such as visualizing an airport on a site without one, modifying an existing airport towards another target airport, or exploring changes in terrain, such as due to climate change or urban development. We present this method a new way to undertake case study, site identification and analysis, as well as undertake speculative design powered by typology informed ML generation, which can be applied to any typologies which could use aerial images to categorize them.
keywords	Airport Development, Machine Learning, GAN, High Dimensional Analysis, Parametric Space Exploration, tSNE, Latent Space Exploration, Data Driven Planning
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_359
id	caadria2023_359
authors	Wang, Xiao, Tang, Peng and Cai, Chenyi
year	2023
title	Traditional Chinese Village Morphological Feature Extraction and Cluster Analysis Based on Multi-source Data and Machine Learning
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. 179–188
doi	https://doi.org/10.52842/conf.caadria.2023.1.179
summary	This study of traditional village morphology provides a possible entry point for understanding the growth patterns of settlements for sustainable development. This study proposes a hybrid data-driven approach to support quantitative morphological descriptions and to further morphology-related studies using open-source map data and deep learning approaches. We construct a dataset of 6819 traditional villages on the Chinese official list with geometrical, geographic and related no-material information. The images containing village buildings combined with roads or other environments are represented in binary to explore the integrated influence of these elements. The neural network is implemented to quantify the morphological features into feature vectors. After dimension reduction, cluster analysis is conducted by calculating the distance between the feature vectors to reveal five main types of Chinese traditional village patterns. The proposed method considers their overall spatial form and other factors such as size, transportation, graphical structure, and density. At the same time, it explores a framework using machine learning in the conservation and renewal work. And it also shows the possibility of data-driven methods for design and decision making.
keywords	Cluster analysis, traditional village, morphology, multi-source data, machine learning, rural development
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2023_71
id	ecaade2023_71
authors	Austern, Guy, Yosifof, Roei and Fisher-Gewirtzman, Dafna
year	2023
title	A Dataset for Training Machine Learning Models to Analyze Urban Visual Spatial Experience
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 781–790
doi	https://doi.org/10.52842/conf.ecaade.2023.2.781
summary	Previous studies have described the effects of urban attributes such as the Spatial Openness Index (SOI) on pedestrians’ experience. SOI uses 3-dimensional ray casting to quantify the volume of visible space from a single viewpoint. The higher the SOI value, the higher the perceived openness and the lower the perceived density. However, the ray casting simulation on an urban-sized sampling grid is computationally intensive, making this method difficult to use in real-time design tools. Convolutional Neural Networks (CNN), have excellent performance in computer vision in image processing applications. They can be trained to predict the SOI analysis for large urban fabrics in real-time. However, these supervised learning models need a substantial amount of labeled data to train on. For this purpose, we developed a method to generate a large series of height maps and SOI maps of urban fabrics in New York City and encoded them as images using colour information. These height map - SOI analysis image pairs can be used as training data for a CNN to provide rapid, precise visibility simulations on an urban scale.
keywords	Visibility Analysis, Machine Learning, CNN, Perceived Density
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_110
id	sigradi2023_110
authors	Bagheriyar, Erfan and Uzun, Can
year	2023
title	Assessment of the Circulation Impact of Furniture in Industrial Buildings through Space Syntax
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 421–432
summary	This paper explores the impact of furniture and machines on spatial organization and circulation systems in industrial buildings using space syntax analysis. While space syntax research covers various settings, industrial buildings have received limited attention. This study aims to address this gap by examining how machines and furniture influence spatial organization and circulation in two industrial buildings: A Nitrile Glove manufacturing facility and a textile manufacturing factory. The furnished and unfurnished floor plans were analyzed using space syntax software, DepthMapX, with connectivity and agent-based analyses. The results indicate that unfurnished plans have centrally located connectivity values, whereas furnished plans create subspaces with varying connectivity. Agent-based analysis reveals that unfurnished spaces have high density in the center, while furnished spaces distribute density more evenly, resulting in more uniform circulation. This study concludes that industrial building spatial configurations result from a combination of architectural design and the placement of machines and furniture.
keywords	Industrial Building, Space Syntax, Connectivity, Agent-Based Analysis, Furnished-Unfurnished plans
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ijac202321208
id	ijac202321208
authors	Ennemoser, Benjamin; Mayrhofer-Hufnagl, Ingrid
year	2023
title	Design across multi-scale datasets by developing a novel approach to 3DGANs
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 358–373
summary	The development of Generative Adversarial Networks (GANs) has accelerated the research of Artificial Intelligence (AI) in architecture as a generative tool. However, since their initial invention, many versions have been developed that only focus on 2D image datasets for training and images as output. The current state of 3DGAN research has yielded promising results. However, these contributions focus primarily on building mass, extrusion of 2D plans, or the overall shape of objects. In comparison, our newly developed 3DGAN approach, using fully spatial building datasets, demonstrates that unprecedented interconnections across different scales are possible resulting in unconventional spatial configurations. Unlike a traditional design process, based on analyzing only a few precedents (typology) according to the task, by collaborating with the machine we can draw on a significantly wider variety of buildings across multiple typologies. In addition, the dataset was extended beyond the scale of complete buildings and involved building components that define space. Thus, our results achieve a high spatial diversity. A detailed analysis of the results also revealed new hybrid architectural elements illustrating that the machine continued the interconnections of scale since elements were not explicitly part of the dataset, becoming a true design collaborator.
keywords	3D Generative adversarial networks, architectural design, Spatial Interpolations
series	journal
last changed	2024/04/17 14:30

_id	acadia23_v3_179
id	acadia23_v3_179
authors	Jabi, Wassim; Leon, David Andres; Alymani, Abdulrahman; Behzad, Selda Pourali; Salamoun, Michelle
year	2023
title	Exploring Building Topology Through Graph Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	Graph theory offers a powerful method for analyzing complex networks and relationships. When combined with machine learning, graph theory can provide valuable insights into the data generated by 3D models. This workshop integrated advanced spatial modeling and analysis with artificial intelligence, highlighting the importance of technological advancements in shaping the future of architecture and design. It introduced participants to novel workflows that link parametric 3D modeling with concepts of topology, graph theory, and graph machine learning. We used Topologicpy, an advanced spatial modeling and analysis software library designed for Architecture, Engineering, and Construction, paired with DGL, a powerful machine learning library that provides tools for implementing and optimizing graph neural networks (Figure 1). In essence, this process blends cutting-edge technologies and architectural principles that will shape the future of design. Participants learned how to use these workflows to convert 3D models into graphs, analyze their properties, and perform classification and regression tasks. Participants also explored how to create synthetic datasets based on generative and parametric workflows, and build and optimize graph neural networks for specific tasks.
series	ACADIA
type	workshop
last changed	2024/04/17 14:00

_id	caadria2023_137
id	caadria2023_137
authors	Jia, Muxin and Narahara, Taro
year	2023
title	Spatial Analytics of Housing Prices With User-Generated POI Data, a Case Study in Shenzhen
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. 635–644
doi	https://doi.org/10.52842/conf.caadria.2023.1.635
summary	Housing is among the most pressing issues in China. Researchers are eager to identify housing property's internal and geographic factors influencing residential property prices. However, few studies have examined the relationship between social media users' point of interest (POI) data and house prices using big data. This paper presents a machine learning model for regression analysis to reveal the relationship between housing prices and check-in POI density in Futian District, Shenzhen. The results show that our proposed price prediction model using additional features based on POI data proved to provide higher prediction accuracy. Our results indicate that incorporating POI features based on current feeds from location-based social networks can provide more up-to-date estimates of housing market price trends.
keywords	Check-in POI, Kernel density estimation, Hedonic pricing method, SVR model
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_253
id	caadria2023_253
authors	Li, Jinze and Tang, Peng
year	2023
title	Multisource Analysis of Big Data on Street Vitality Using GIS Mapping and Deep Learning: A Case Study of Ding Shu, China
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. 565–574
doi	https://doi.org/10.52842/conf.caadria.2023.1.565
summary	Urban vitality is the driving force behind sustainable urban development. As the most frequently used public space in cities, the enhancement of street vitality is of great significance for improving human-centred habitats. Based on multi-source big data, this study uses spatial and statistical analysis methods to explore the impact factors of street vitality. Through the quantitative evaluation of these factors, we propose corresponding strategies to enhance the vitality of the street. Firstly, the spatial elements of streets are extracted using deep learning algorithm based on the acquired street view images. Further, the impact factors of street vitality are demonstrated using statistical methods by combining multi-source data. We established an evaluation system based on the impact factors of street vitality, which can quantify and predict street vitality. In this way, we can propose vitality enhancement strategy for the street with lower vitality in a targeted approach. The feasibility of the process is demonstrated by using Ding Shu as an example. This study provides a basic framework for a people-centred approach to enhance street vitality based on big data. It also contributes to causal inference in urban problems.
keywords	Multi-source data, street vitality, deep learning, spatial analysis, statistical analysis, causal inference, people-centred city
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2024_46
id	ecaade2024_46
authors	Talmor-Blaistain, Anat; Merhav, Maayan; Fisher-Gewirtzman, Dafna
year	2024
title	Grid to Star Network Transformation: Developing a Topological Assessment and Transformation Model to Enhance Spatial Memory and Route Learning for Wayfinding
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 329–338
doi	https://doi.org/10.52842/conf.ecaade.2024.2.329
summary	Wayfinding is the cognitive process of determining and following a path from one location to another. During navigation, a route-learning process occurs in which individuals encode spatial information. Older populations and individuals with cognitive difficulties face challenges in spatial learning and navigating complex environments. This study builds on Merhav and Fisher-Gewirtzman (2023), which suggests that star-shaped pedestrian paths, containing a distinct center through which all paths pass between origins and destinations, improve spatial memory and learning abilities for wayfinding compared to grid networks, benefiting all age groups. The research aims to bridge the gap in the analysis of pedestrian network topological shapes by developing a quantitative analytical model to evaluate how close each network is to a grid-type or a star-type and potentially transform these networks, from a grid-type into star-type topology. The proposed model suggests a methodology for assessing and modifying network topologies through spatial manipulations. The model utilizes a combination of open-source components (such as Space Syntax axial analysis and the Galapagos optimization plugin) and combines novel computational tools (python code) to rank nodes in the network and identify networks where isolated areas were created during the optimization process.
keywords	Spatial Computing, Spatial memory, Route learning, Wayfinding, Grid and star network’s Topological Shape, Space Syntax, analytical model
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2023_186
id	ecaade2023_186
authors	Yonder, Veli Mustafa, Dogan, Fehmi, Cavka, Hasan Burak, Tayfur, Gokmen and Dulgeroglu, Ozum
year	2023
title	Decoding and Predicting the Attributes of Urban Public Spaces with Soft Computing Models and Space Syntax Approaches
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 761–768
doi	https://doi.org/10.52842/conf.ecaade.2023.2.761
summary	People spend a considerable amount of time in public spaces for a variety of reasons, albeit at various times of the day and during season. Therefore, it is of utmost importance for both urban designers and local authorities to try to gain an understanding of the architectural qualities of these spaces. Within the scope of this study, squares and green parks in Izmir, the third largest city in Turkey, were analyzed in terms of their dimensions, landscape characteristics, the quality of their semi-open spaces, their landmarks, accessibility, and overall aesthetic quality. Using linear predictor, general regression neural networks, multilayer feed-forward neural networks (2-3-4-5-6 nodes), and genetic algorithms, soft computing models were trained in accordance with the results of the conducted analyses. Meanwhile, using space syntax methodologies, a visibility graph analysis and axial map analysis were conducted. The training results (i.e., root mean square error, mean absolute error, bad prediction rates for testing and training phases, and standard deviation of absolute error) were obtained in a comparative table based on training times and root mean square error values. According to the benchmarking table, the network that most accurately predicts the aesthetic score is the 2-node MLFNN, whereas the 6-node MLFN network is the least successful network.
keywords	Multilayer Perceptron, Architectural Aesthetics, General Regression Neural Net, Spatial Configuration
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia23_v2_408
id	acadia23_v2_408
authors	C Kim, Frederick; Johanes, Mikhael; Huang, Jeffrey
year	2023
title	Flow2Form: A Flow-Driven Computational Framework for Early Stage Architectural Design
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 979-8-9860805-9-8]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 408-418.
summary	Flows have been a persistent theme as a rational and formal basis for architecture. This paper introduces a flow-based design framework for architecture using parametric modeling and machine learning analysis. It explores the integration of flows’ rational and figurative aspects into the early stages of the design process. The research employs para- metric tools and machine learning algorithms to represent and analyze flows, focusing on the artisanal and craft processes aiming for circular proto-typology as a transfor- mative architecture. The framework involves three stages: 3D flow modeling, machine learning analysis of formal and topological properties, and process-based programming and optimization. The results include volumetric representations of 16 artisanal flows and the classification of nodes based on their formal and topological characteristics. The framework enables the exploration of flow-driven architectural design, and bridges the gap between human interpretation and computational design. The research contributes to understanding flows to form in architecture, and the potential of machine learning in shaping architectural space.
series	ACADIA
type	paper
email
last changed	2024/04/17 13:59

_id	caadria2023_50
id	caadria2023_50
authors	Jiang, Mingrui and Cai, Chenyi
year	2023
title	Communication With Detroit: Machine Learning in Open Source Community Housing Design
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. 49–58
doi	https://doi.org/10.52842/conf.caadria.2023.1.049
summary	Traditional pre-design investigation includes conceptual studies, site analysis, and programming processes to analyze the site and design for users. Instead, designers and architects should consider users' ideas and their actual usage of space, which are recorded and reflected on the social media platform. To introduce more citizens' voices in the design and learn more about people's expression of Detroit city and its housing, we propose to involve the machine learning analysis in the earlier stage of the housing project using users' reflections from social media to support the conceptual design. This paper introduces a novel design framework that deals with the lacking public programs in Detroit using an online data clustering platform and demonstrates a conceptual open-source community housing design according to related findings. This framework incorporates data collection from the Twitter platform, implementation of clustering for user-oriented programs, and design applications based on the findings. Our research demonstrates an efficient and flexible approach to the open-source community housing project.
keywords	Machine learning, Decision making, Social Media, User-oriented design, Open source community
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	cdrf2023_273
id	cdrf2023_273
authors	Pixin Gong, Xiaoran Huang, Chenyu Huang, Shiliang Wang
year	2023
title	Modeling on Outdoor Thermal Comfort in Traditional Residential Neighborhoods in Beijing Based on GAN
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_23
summary	With the support of new urban science and technology, the bottom-up and human-centered space quality research has become the key to delicacy urban governance, of which the Universal Thermal Climate Index (UTCI) have a severe influence. However, in the studies of actual UTCI, datasets are mostly obtained from on-site measurement data or simulation data, which is costly and ineffective. So, how to efficiently and rapidly conduct a large-scale and fine-grained outdoor environmental comfort evaluation based on the outdoor environment is the problem to be solved in this study. Compared to the conventional qualitative analysis methods, the rapidly developing algorithm-supported data acquisition and machine learning modelling are more efficient and accurate. Goodfellow proposed Generative Adversarial Nets (GANs) in 2014, which can successfully be applied to image generation with insufficient training data. In this paper, we propose an approach based on a generative adversarial network (GAN) to predict UTCI in traditional blocks. 36000 data samples were obtained from the simulations, to train a pix2pix model based on the TensorFlow framework. After more than 300 thousand iterations, the model gradually converges, where the loss of the function gradually decreases with the increase of the number of iterations. Overall, the model has been able to understand the overall semantic information behind the UTCI graphs to a high degree. Study in this paper deeply integrates the method of data augmentation based on GAN and machine learning modeling, which can be integrated into the workflow of detailed urban design and sustainable construction in the future.
series	cdrf
email
last changed	2024/05/29 14:04

_id	acadia23_v2_596
id	acadia23_v2_596
authors	Ran, Wuwu; Yin, Lu; Yu, Jie
year	2023
title	Machine Learning-driven Comparative Study: Morphological Taxonomy in Screen-Based Building Clusters
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 979-8-9860805-9-8]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 596-605.
summary	Our framework employs a convolutional autoencoder model to integrate urban morphology data and attribute vectors of screen-based buildings, generating final feature vectors that are subsequently used for clustering and comparative analysis. We carried out empirical testing of our methods using data from the cities of Chongqing and Shanghai in China. This comparative study identifies various categories of screen clusters, and demonstrates the framework’s effectiveness. The primary objective of this research is to elucidate the similarities and differences among screen-based building clusters, aiming to provide architects and urban designers with a more comprehensive understanding of the typological and topological characteristics of augmented space syntax. Through this approach, we hope to contribute to the development of more effective design strategies and policies for the implementation and integration of screen-based building clusters in urban environments.
series	ACADIA
type	paper
email
last changed	2024/04/17 13:59

_id	ecaade2023_378
id	ecaade2023_378
authors	Araya, Sergio, Fuentes, Cesar, Strahlendorff, Mikko, Camus, María Jesus and Kröger, Anni
year	2023
title	Three-Dimensional Realtime Air Quality Mapping using Astronomical algorithms on Urban Environments
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 811–820
doi	https://doi.org/10.52842/conf.ecaade.2023.2.811
summary	The OMS estimates that over 7 million people die every year of complications attributed to atmospheric pollution. Air quality has degraded progressively and dramatically in urban environments over the last couple of decades, being a current concern in most metropolitan areas, and the focus of public policy as well as public/private scientific innovation for better diagnostics and better solutions. At SIC we are developing a method for 3D mapping the sources, affected locations, density, motion, translation, and potential composition of polluted air masses in close to real-time. We do this by leveraging a multidisciplinary approach that encompasses urban and architectural simulation with data science and astronomical techniques, producing a data visualization that enables novel research in air quality, urban policy, private investment, sustainability efforts, and smart transportation. Our approach, Sit-C, combines satellite remote sensing of air masses and atmospheric conditions, with data obtained from traffic and urban surveillance cameras deployed throughout the city of Santiago, in Chile. These cameras, oftentimes open to public access, are usually placed linearly along main avenues, or scattered around urban milestones, providing walk-though perspectives and locally situated POVs to observe the city, analog to series of cross-sections through urban areas. Satellite sensing provides a large-scale plan view, allowing for precise location of specific conditions across a region. This collaboration between architects, designers, engineers, and meteorologists, from Chile and Finland, combines digital design, data science, and remote sensing techniques to study air quality. We study suspended particulate matter (SPM) and other molecules, and its spatial behavior over time, through light-occlusion analysis, producing a three-dimensional map of the air over a city.
keywords	Air Quality, Pollution, 3D mapping, Data Science, Astronomy, Sustainable Cities, Smart Cities, Machine Learning
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_84
id	caadria2023_84
authors	Chen, Bowen, Lao, Pui Kuan, Dou, Zhiyi, Qiu, Wai-Shan and Luo, Dan
year	2023
title	Analyst Patterns of Influence Between a Commercial Distribution and Neighbourhood Dynamic in a Residential Neighbourhood
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. 525–534
doi	https://doi.org/10.52842/conf.caadria.2023.1.525
summary	The spatial distribution of urban commercial spaces significantly impacts the overall efficiency and vibrancy of adjacent neighbourhoods. As such, it is an important factor to consider during urban development. This study aims to examine the patterns of impact between commercial distributions and neighbourhood dynamics in a residential neighbourhood, based on the case study of a highly populated, thriving commercial, and culturally rich area situated in Mong Kok, Hong Kong. In this research, a series of numeric evaluations and statistical analyses of liveability and vibrancy metrics are presented, uncovering the tension created by existing commercial forms and local living patterns. This research started with multi-dimensional data mining, such as accessing planning data using Geographic Information Systems (GIS), perception data using Street View Images (SVI), and business performance data from Google; secondly, analysing the data via machine learning (ML) algorithm and statistical correlation to identify correlations overlaid with a mapping of spaces of measurable characteristics. The goal is to establish a measurable evaluation of the relationship between commercial vibrance and urban features that can further inform the impact of urban design strategies on fostering the vitality of community commercial centres.
keywords	Mong Kok, Model Learning Machine (ML), SVM, PSPnet, MaskRCNN, POI, Commercial vibrance, Heatmap correlation, Visualization, QGIS, Google Maps Information
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	cdrf2023_201
id	cdrf2023_201
authors	Chunxia Yang, Ming Zhan
year	2023
title	Construction of Recreation Behavior Simulation Model of Public Space in Urban Waterfront—Taking Huangpu River in Shanghai as an Example
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_17
summary	This study constructs a multi-agent behavior simulation model to explore the quantitative simulation method of waterfront public space. Taking 6 waterfront public space samples along the Huangpu River in Shanghai as research objects, this study first collects environmental data and pedestrian behavior data through field survey, and then analyzes and processes the data to obtain the Spatial Attraction Weight (SWA) that expresses the relationship between pedestrian behavior and spatial elements. Then, based on the Anylogic platform, the pedestrian agent particles expressing people’s characteristics are placed into the simulation environment based on the social force model. They interact in real time to dynamically simulate the pedestrian’s behavior. Finally, fitting verification of the preliminary model is carried out. The qualitative comparison and quantitative correlation analysis are combined to enhance the accuracy. The behavior simulation model of waterfront public space built in the study can more realistically represent the pedestrian's behavior. It can realize the scientific prediction of the future use of waterfront space and provide more detailed reference for problem diagnosis and optimization.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ijac202321109
id	ijac202321109
authors	Correa, Sara Dotta; Carlos Eduardo Verzola Vaz
year	2023
title	A shape grammar for the spontaneous occupation settled in fishing villages: The case of Garopaba, Brazil
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 1, pp. 158–187
summary	This work presents the second stage of a larger study which conducted the elaboration of a shape grammar based on the analysis of the remaining fishing villages in the Santa Catarina State, Southern Brazil. The architectural and formal structure of the villages provide a perspective of the constructive methods and features embedded in the spatial arrangements and represent a local cultural heritage site. The aim of the present work relies on unraveling its spatial resources using a shape grammar approach, in order to contribute to the preservation of a distinct culture, and is intended to be used to safeguard its formal language planning. Also, through the analytical grammar, it would be possible to contribute in framing design guidelines regarding the remaining villages, to keep it steady on the traditional context, safeguarding its peculiar character. As such, the method in this body of work considered the analysis resulting from the first stage, in order to develop an analytical shape grammar using Garopaba village as a case of study. This stage involved the establishment of a formal language for the analytical grammar, consisting of initial form, vocabulary, families, and spatial relations, and thus, a series of rules emerged, decoding the composition arrangements. The results involved the verification of the considered parameters, confirming the existence of spatial patterns. Thus, it was possible to conclude that the grammar contributes to identifying the occupation process occupation and the spatial patterns which underlies an artisanal fishing village. Also, the grammar allowed the creation of urban compositions similar to the structure of traditional fishing communities in Santa Catarina, generating different discussions regarding the analytical approach towards fishing villages arrangements, compared to the examples studied, specifically, in Garopaba city
keywords	shape grammar, urban form, fishing villages, Santa Catarina
series	journal
last changed	2024/04/17 14:30

_id	ecaade2023_138
id	ecaade2023_138
authors	Crolla, Kristof and Wong, Nichol
year	2023
title	Catenary Wooden Roof Structures: Precedent knowledge for future algorithmic design and construction optimisation
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 611–620
doi	https://doi.org/10.52842/conf.ecaade.2023.1.611
summary	The timber industry is expanding, including construction wood product applications such as glue-laminated wood products (R. Sikkema et al., 2023). To boost further utilisation of engineered wood products in architecture, further development and optimisation of related tectonic systems is required. Integration of digital design technologies in this endeavour presents opportunities for a more performative and spatially diverse architecture production, even in construction contexts typified by limited means and/or resources. This paper reports on historic precedent case study research that informs an ongoing larger study focussing on novel algorithmic methods for the design and production of lightweight, large-span, catenary glulam roof structures. Given their structural operation in full tension, catenary-based roof structures substantially reduce material needs when compared with those relying on straight beams (Wong and Crolla, 2019). Yet, the manufacture of their non-standard geometries typically requires costly bespoke hardware setups, having resulted in recent projects trending away from the more spatially engaging geometric experiments of the second half of the 20th century. The study hypothesis that the evolutionary design optimisation of this tectonic system has the potential to re-open and expand its practically available design solution space. This paper covers the review of a range of built projects employing catenary glulam roof system, starting from seminal historic precedents like the Festival Hall for the Swiss National Exhibition EXPO 1964 (A. Lozeron, Swiss, 1964) and the Wilkhahn Pavilions (Frei Otto, Germany, 1987), to contemporary examples, including the Grandview Heights Aquatic Centre (HCMA Architecture + Design, Canada, 2016). It analysis their structural concept, geometric and spatial complexity, fabrication and assembly protocols, applied construction detailing solutions, and more, with as aim to identify methods, tools, techniques, and construction details that can be taken forward in future research aimed at minimising construction complexity. Findings from this precedent study form the basis for the evolutionary-algorithmic design and construction method development that is part of the larger study. By expanding the tectonic system’s practically applicable architecture design solution space and facilitating architects’ access to a low-tech producible, spatially versatile, lightweight, eco-friendly, wooden roof structure typology, this study contributes to environmentally sustainable building.
keywords	Precedent Studies, Light-weight architecture, Timber shell, Catenary, Algorithmic Optimisation, Glue-laminated timber
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_387
id	sigradi2023_387
authors	Dong, Jiahua, Lin, Shuiyang and van Ameijde, Jeroen
year	2023
title	Predicting Network Integration Based on Satellite Imagery Around High-Density Public Housing Estates Through Machine Learning
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 795–806
summary	In studies focusing on environmental and health aspects of urban planning, the integration of road networks within the built environment emerges as an important metric for assessing the livability and healthiness of neighborhoods. The complexity and diversity of the road networks are significant for shaping vibrant streets. In Hong Kong’s ongoing construction program of large-scale public housing estates, the design prioritizes the connectivity of pedestrian circulation to foster social interaction among residents and encourage the utilization of recreational facilities. In this study, an analytical framework is developed to interpret public housing estate spatial layout based on satellite imagery. It extracts road networks using neural networks and vectorizes results to analyze network integration around estates to predict social interactions. The aim of this process is to employ a machine learning workflow to analyze options for newly planned estates, where the design configuration can be further optimized based on its potential to stimulate social engagement and community interaction. Due to the scalability and universality of the method, the research can contribute to improved road networks and sociable housing complexes in Hong Kong, or in other international cities of similar density and vibrancy.
keywords	Network Integration, Spatial Structure, Satellite Imagery, Machine Learning, Hong Kong Public Housing
series	SIGraDi
email
last changed	2024/03/08 14:07

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