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	cdrf2022_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

_id	cdrf2022_25
id	cdrf2022_25
authors	Hao Zhang, Yuetao Wang, Yuhan Tan, and Jilong Zhao
year	2022
title	Parametric Skin Design Method Based on Plane Crystallographic Group Operation Principle
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_3
summary	Under the dual constraints of industrialization and digitalization, the building skin and structure are further integrated to form standardized units to meet the requirements of architectural performance, industrial prefabrication and “complexity” aesthetic characteristics. The complex and diverse forms of today's building skin hide profound mathematical logic relations and operation rules of form generation. Crystallographic group with regular symmetry and the operation principles reflected by it is one of the most important rules and methods of form and pattern processing in skin design. The study of the mural symbols in ancient Egypt, the murals in the Alhambra, the manuscripts of Escher and the window lattice in ancient Chinese architecture profoundly reflects the basic operation principle of crystal group in shaping the skin form of architecture. Abundant and diverse architectural skin forms can be formed through the operation of symmetry group on basic graphic units. On the basis of clarifying the basic principle of crystal group action, the operation matrix of crystallographic symmetry group can be transformed into parameterized operation steps through programming language for visual operation, and then the skin form with high complexity and leap dimension can be generated by geometric algorithm, and the design method of building skin generation based on crystallographic group is constructed. In the selection of operation form, combined with the calculation of building performance and structure, the construction skin can be used in practical engineering is generated. Based on crystallographic group operation, the unifications of building skin and the classification simplification of components can meet the requirements of modular and unifications design in the process of building industrialization, and meet the requirements of current building industrialization and digitization. It has great research significance and value in the aspects of design and construction efficiency and material economic cost.
series	cdrf
email
last changed	2024/05/29 14:02

_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	acadia22_714
id	acadia22_714
authors	Li, Yunqin; Zhang, Jiaxin; Wang, Xueqiang; Ma, Kai
year	2022
title	Measuring Street Vitality Based on Video-image Using Deep Learning
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. 714-725.
summary	This paper proposes a deep convolutional neural network-based framework for fine-scale studies on automatic evaluation of street-level vitality using multiple object tracking and image segmentation with video data. A deep learning model for street vitality evaluation was proposed based on the intensity and complexity of pedestrian activities.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	cdrf2022_233
id	cdrf2022_233
authors	Yubo Liu, Zhilan Zhang, and Qiaoming Deng
year	2022
title	Exploration on Diversity Generation of Campus Layout Based on GAN
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_20
summary	Previous studies have shown that GAN has made some progress in the generation of campus layout plan, but the result is single output for single input condition. This paper hopes to make some attempts and explorations on the diversity generation of campus planning layout design by machine learning. Based on Pix2Pix model, this paper proposes a method to divide image channels so that both the campus function bubble diagram and the site boundary can both become the input conditions. There is a strong correspondence between the campus functional bubble diagram and the campus layout. The main idea of this study is to control the generated results by changing the input of the campus functional bubble diagram, so that we can have a diversity layout of campus according to the same site conditions. In the experiment, we train thirty samples of campus planning layout design, and finally evaluate the generated results in a qualitative and quantitative way, which proves that the generated results are relatively ideal. This research enables designers to participate in the process of machine learning generative design to control the generation results.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_299
id	caadria2022_299
authors	Cui, Qiang, Zhang, Huikai, Pawar, Siddharth Suhas, Yu, Chuan, Feng, Xiqiao and Qiu, Song
year	2022
title	Topology Optimization for 3D-Printable Large-Scale Metallic Hollow Structures With Self-Supporting
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. 101-110
doi	https://doi.org/10.52842/conf.caadria.2022.2.101
summary	Design for Additive Manufacturing (DfAM), is a one of the most commonly used and foundational techniques used in the development of new products, and particularly those that involve large-scale metallic structures composed of hollow components. One such AM technique is Wire Arc Additive Manufacturing (WAAM), which is the application of robotic welding technology applied to Additive Manufacturing. Due to the lack of a simple method to describe the fabricating constraint of WAAM and the complex hollow morphology, which difficultly deploys topology optimization structural techniques that use WAAM. In this paper, we develop a design strategy that unifies ground-structure optimization method with generative design that considers the features of hollow components, WAAM overhang angle limits and manufacturing thickness limits. The method is unique in that the user can interact with the design results, make changes to parameters, and alter the design based on the user‚s aesthetic or specific manufacturing setup needs. We deploy the method in the design and 3D printing of an optimized Electric Vehicle Chassis and successfully test in under different loading conditions.
keywords	Topology optimization, Generative design, Self-supporting, Hollow structures, Metallic 3D printing, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_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	caadria2022_488
id	caadria2022_488
authors	Guo, Zhe, Zhang, Zihuan and Li, Ce
year	2022
title	Robotic Carving Craft, Research on the Application of Robotic Carving Technology in the Inheritance of Tradition-Al Carving Craft
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. 747-756
doi	https://doi.org/10.52842/conf.caadria.2022.1.747
summary	In order to realize the inheritance of handicraft skills via digital fabrication technique, so as to preserve the traditional construction culture, this paper discusses a method of control industrial robot (six-axis KUKA kr-60 robotic arm) simulate carving craftsmen working process and explores the relationship between carving posture and different clay states. This paper starts with discussion with cultural heritage in the background of digital tools application. Next, a method to determine the pose of robotic arm by giving the angle value of the six axis is applied in the subsequent carving experimental research, which can make the robotic arm have a smoother and reasonable motion performance by disable the redundant axis movement of the robotic arm when adjusting those poses. Then, a series of carving experiments has been carried out to explore the connection between robotic movement and carved detail, together with a carving path arrangement method that allow for specific carved lines caused by given axis value. This research shows the possibility to create complex form through defining robot movement, which could fundamentally make robot manufacturing a new formal meaning.
keywords	Clay Carving, Robotic Arm Control, Crafts Inheritance, Form Algorithm, SDG 8
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	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_177
id	caadria2022_177
authors	Pan, Yongjie and Zhang, Tong
year	2022
title	Outdoor Thermal Environment Assessment of Existing Residential Areas Supported by UAV Thermal Infrared and 3D Reconstruction Technology
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. 729-738
doi	https://doi.org/10.52842/conf.caadria.2022.2.729
summary	The underlying surface temperature is an effective evaluation index to study the urban micro-scale thermal environment. For surface temperature acquisition, the thermal infrared camera mounted on a unmanned aerial vehicle (UAV) can reduce field work intensity, improve data collection efficiency, and ensure high accuracy at low cost. In order to convert the 2D thermal image into a more intuitive 3D thermal model, the UAV-based thermal infrared 3D reconstruction is adopted. The key element of thermal infrared 3D model reconstruction lies in the processing of thermal infrared images with low resolution and different temperature scales. In order to improve the quality of the final thermal 3D model, this paper proposes the reconstruction of the detailed 3D mesh using visible images (higher resolution), and map then mapping thermal textures onto the mesh using thermal images (low resolution). In addition, absolute temperature values are extracted from thermal images with different temperature ranges to ensure consistence between color and temperature values in the reconstructed thermal 3D model. The thermal 3D model generated for an existing residential area in Nanjing successfully displays the temperature distribution of the underlying surface and provides a valuable basis for outdoor thermal environment assessment.
keywords	Thermal image, UAV, 3D reconstruction, Residential outdoor space, Underlying surface temperature, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_255
id	caadria2022_255
authors	Wu, Zihao, Zhang, Yunsong and Tong, Ziyu
year	2022
title	Quantification of the Thermal Environmental Value of Urban Pores: A Case Study of Nanjing
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. 719-728
doi	https://doi.org/10.52842/conf.caadria.2022.2.719
summary	The term "Urban pores‚ refers to the space formed by the enclosure of buildings, which have great value for regulating the microclimate. Many previous studies have focused only on a single urban pore section, ignoring the spatial distribution at the urban scale. In this study, the openness of urban pores in Nanjing was quantified and grouped, and then the spatial distribution characteristics of each openness group were further calculated. Based on this, the study combined the spatial distribution characteristics of urban pores with urban thermal environment data and an LCZ urban form classification model to analyse the impact of urban pores on the urban thermal environment. The results show that 1) the impact of urban pores is greater in summer and autumn, where its spatial agglomeration has a higher cooling value for the urban thermal environment, while this is not significant in winter; 2) the spatial agglomeration of urban pores in the high openness group, mid-high openness group and mid-low openness group have a higher cooling effect, which mainly corresponds to water, open spaces or parks and urban roads. These spaces should be given more attention when developing urban design strategies. The results can provide some references for urban development.
keywords	urban pores, openness, spatial distribution, urban thermal environment, local climate zone (LCZ), SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_175
id	cdrf2022_175
authors	Xingzhao Zhang, Xinyu Wu, Luqiao Yang, Jiaqi Xu, Ruizhe Luo, and Jiawei Yao
year	2022
title	Effect of Morphological Indicators on the Pedestrian Level Wind of the Existing Workers Villages in Shanghai
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_15
summary	The workers villages are typical residential type during Shanghai’s urbanization built from the 1950s to the 1980s. Due to changes in the urban environment and climatic circumstances, the workers villages have inadequate natural ventilation and difficulty in dispersing pollutants, putting residents’ health at risk. In the context of urban renewal, it is necessary to clarify the effect of building morphological indicators on pedestrian level wind, especially in such old residential communities. In this paper, 100 workers villages representatives were gathered by GIS. Their summer ventilation conditions were simulated using the CFD solving the LES turbulence equation. The correlation between 9 morphological indicators and 2 pedestrian level wind indicators was obtained quantitatively by Pearson analysis and regression analysis. The result shows increasing the building coverage of 0.94% in the workers villages, the ratio of the area of the static wind in summer will increase subsequently by 10%. The results highlight the importance of considering morphological indicators to enhance the wind environment, and provide suggestions for the environmental transformation of communities with similar characteristic in the high-density city.
series	cdrf
email
last changed	2024/05/29 14:02

_id	cdrf2022_138
id	cdrf2022_138
authors	Ze Zhang and Zhengwang Wu
title	A Sunlight Duration Time Driven Multi-objective Optimization Method for the Layout of High-Rise Residential Quarters Based on NSGA2 Algorithm
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_12
summary	Extending sunlight duration time by optimizing the layout of the high-rise residential quarters during the early design stage is one of the most effective approaches to reducing carbon emissions. This paper proposes a multi-objective optimization method for high-rise residential quarter layout based on the NSGA2 algorithm. The method is aimed to maximize the first floor’s sunlight duration time and its uniformity both. A simulated plot in Xiamen is taken as an example for multi-objective optimization. After the optimization, the layouts are analyzed and the better one is selected. The results show that the proposed method can achieve higher overall sunlight duration and its uniformity rate and maximize floor area ratio in the early design phase. However, the proposed method has its drawbacks. This method requires the pre-design of the building plan. The algorithm generates a lot of invalid solutions during the optimization. The optimization time increases dramatically with the quantity increase of input parameters. According to the above, there is still room for improvement in the proposed method.
email
last changed	2024/05/29 14:02

_id	caadria2022_264
id	caadria2022_264
authors	Zhang, Garry Hangge, Meng, Leo Lin, Gardner, Nicole, Yu, Daniel and Haeusler, Matthias Hank
year	2022
title	Transit Oriented Development Assistive Interface (TODAI): A Machine Learning Powered Computational Urban Design Tool for TOD
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. 253-262
doi	https://doi.org/10.52842/conf.caadria.2022.1.253
summary	Transit-oriented Development(TOD) is widely regarded as a sustainable development paradigm for its sensible space planning and promotion of public transit access. Research in providing decision support tools of TOD may contribute to the Sustainable Development Goals, especially towards sustainable cities and communities (SDG goal 11).While the existing Geographic Information System(GIS) approach may well inform TOD planning, computational design, simulation, and visualisation techniques can further enhance this process. The research aims to provide a data-driven, computational-aided planning support system (PSS) to enhance the TOD decision-making process. The research adopts an action research methodology, which iteratively designs experiments and inquires through situating the research question in real-world practice. A work-in-progress prototype is provided - Transit-Oriented Development Assistive Interface (TODAI), along with an experiment in a newly proposed metro station in Sydney, Australia. TODAI provides real-time visualisation of urban forms and analytical data indicators reflecting key considerations relevant to TOD performance. A regressive machine learning model (XGBoost) is used to make predictions of analytical indicators, promptly producing outcomes that may otherwise require a costly computational operation.
keywords	TransUrban Planning, Transit-Oriented Development, Planning Support System, Machine Learning, SDG 11it-Oriented Development, Urban Planning, Machine Learning, Computational Design, SDG11, Sustainable Cities and Communities
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_291
id	caadria2022_291
authors	Zhang, Qiyan, Li, Biao, Mo, Yichen, Chen, Yulong and Tang, Peng
year	2022
title	A Web-based Interactive Tool for Urban Fabric Generation: A Case Study of Chinese Rural Context
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. 625-634
doi	https://doi.org/10.52842/conf.caadria.2022.1.625
summary	The design of rural fabric is significant for making sustainable communities and requires innovative design models and prospective work paths. This paper presents an interactive tool based on the web to generate block fabric that responds to the Chinese rural context, consisting of streets, plots, and buildings. The tool is built upon the Browser/Server (B/S) architecture, allowing users to access the generation system via the web simply and to have interactive control over the generation process in a user-friendly way. The underlying tensor field and rule-based system are adopted in the backend to model the fabric subject to multiple factors, with rules extracted from the rural design prototype. The system aims to integrate the procedural model with practical design constraints in the rural context, such as patterns, natural boundaries, elevations, planning structure, and existing streets. The proposed framework supports extensions to different urban or suburban areas, inspiring the promising paths of remote cooperation and generative design for sustainable cities and communities.
keywords	Generative Design, Web-based Tool, Urban Fabric, Rural Context, Procedural Modeling, Tensor Field, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_125
id	cdrf2022_125
authors	Zihuan Zhang, Zao Li, and Zhe Guo
year	2022
title	Research on Real-Time Interactive Spatial Element Optimization Method Based on EEG Signal—Taking Indoor Space Color and Window Opening Size as the Optimization Object
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_11
summary	In recent years, the research on digital design and perceptual evaluation has gradually become a hot topic in the field of digital design. Based on digital space optimization theory and perceptual evaluation tools, this study attempts to establish an optimization method to optimize built space elements in real-time using human psychological indicators. This method takes the specific indicators of the Meditation value and Attention value in the human EEG signal analyzed by the TGAM module as the optimization objective, the architectural space color and the window size as the optimization object, and the multi-objective genetic algorithm as the optimization tool. To realize this optimization method, this research combines virtual reality scene and parametric linkage model to establish tool platform and workflow. Taking the optimization of typical residential space as an example by recruiting 50 volunteers to participate in the experiment, this study concludes that this method is effective and feasible through experiment and quantitative analysis of experimental results and lays the foundation for more EEG indicators and more complex spatial element optimization research in the future.
series	cdrf
email
last changed	2024/05/29 14:02

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