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	ascaad2021_021
id	ascaad2021_021
authors	Albassel, Mohamed; Mustafa Waly
year	2021
title	Applying Machine Learning to Enhance the Implementation of Egyptian Fire and Life Safety Code in Mega Projects
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 7-22
summary	Machine Learning has become a significant research area in architecture; it can be used to retrieve valuable information for available data used to predict future instances. the purpose of this research was to develop an automated workflow to enhance the implementation of The Egyptian fire & life safety (FLS) code in mega projects and reduce the time wasted on the traditional process of rooms’ uses, occupant load, and egress capacity calculations to increase productivity by applying Supervised Machine Learning based on classification techniques through data mining and building datasets from previous projects, and explore the methods of preparation and analyzing data (text cleanup- tokenization- filtering- stemming-labeling). Then, provide an algorithm for classification rules using C# and python in integration with BIM tools such as Revit-Dynamo to calculate cumulative occupant load based on factors which are mentioned in the Egyptian FLS code, determine classification and uses of rooms to validate all data related to FLS. Moreover, calculating the egress capacity of means of egress for not only exit doors but also exit stairs. In addition, the research is to identify a clear understanding about ML and BIM through project case studies and how to build a model with the needed accuracy.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	caadria2021_161
id	caadria2021_161
authors	Zhao, Xin, Han, Yunsong and Shen, Linhai
year	2021
title	Multi-objective Optimisation of a Free-form Building Shape to improve the Solar Energy Utilisation Potential using Artificial Neural Networks
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 221-230
doi	https://doi.org/10.52842/conf.caadria.2021.1.221
summary	Optimisation of free-form building design is more challenging in terms of building information modelling and performance evaluation compared to conventional buildings. The paper provides a Photogrammetry-based BIM Modelling - Machine Learning Modelling - Multi-objective Optimisation framework to improve the solar energy utilisation potential of free-form buildings. Low altitude photogrammetry is used to collect the building and site environmental information. An ANN prediction model is developed using the control point coordinates and simulation data. Through parametric programming, the multi-objective algorithm is coupled with the ANN model to obtain the trade-off optimal building form. The results show that the maximum solar radiation value in winter can increase by 30.60% and the minimum solar radiation in summer can decrease by 13.99%. It is also shown that the integration of ANN modelling and photogrammetry-based BIM modelling into the multi-objective optimisation method can accelerate the optimisation process.
keywords	Multi-objective optimisation; Artificial neural network; Free-form shape building ; Solar energy utilisation
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2021_216
id	caadria2021_216
authors	Aman, Jayedi, Tabassum, Nusrat, Hopfenblatt, James, Kim, Jong Bum and Haque, MD Obidul
year	2021
title	Optimizing container housing units for informal settlements - A parametric simulation & visualization workflow for architectural resilience
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 51-60
doi	https://doi.org/10.52842/conf.caadria.2021.1.051
summary	In rapidly growing cities like Dhaka, Bangladesh, sustainable housing in urban wetlands and slums present a challenge to more affordable and livable cities. The Container Housing System (CHS) is among the latest methods of affordable, modular housing quickly gaining acceptance among local stakeholders in Bangladesh. Even though container houses made of heat-conducting materials significantly impact overall energy consumption, there is little research on the overall environmental impact of CHS. Therefore, this study aims to investigate the performance of CHS in the climatic context of the Korail slum in Dhaka. The paper proposes a building envelope optimization and visualization workflow utilizing parametric cluster simulation modeling, multi-objective optimization (MOO) algorithms, and virtual reality (VR) as an immersive visualization technique. First, local housing and courtyard patterns were used to develop hypothetical housing clusters. Next, the CHS design variables were chosen to conduct the MOO analysis to measure Useful Daylight Illuminance and Energy Use Intensity. Finally, the prototype was integrated into a parametric VR environment to enable local stakeholders to walk through the clusters with the goal of generating feedback. This study shows that the proposed method can be implemented by architects and planners in the early design process to help improve the stakeholders understanding of CHS and its impact on the environment. It further elaborates on the implementation results, challenges, limitations of the parametric framework, and future work needed.
keywords	Multi-objective Optimization; Building Energy Use; CHS; Informal Settlements; Parametric VR
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2021_136
id	caadria2021_136
authors	Carallo, Marinella
year	2021
title	Office building design in Hong Kong Island through shape optimization
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 441-450
doi	https://doi.org/10.52842/conf.caadria.2021.1.441
summary	Dealing with crucial decision-making process has led to the development of many different methods of multicriteria assessments, especially optimization methodologies. This work is mainly focused on the integration of advanced computational design and digital methods, to design a complex building shape resulting in a performance-based approach through optimization methodologies. The project consists of the design of a skyscraper in Hong Kong Island made through parametrically controlled shape and evaluated respect to light and wind to reduce Urban Heat Island phenomena and enhance liveability. The aim is to find out a unique methodology that can be applied to different cases by making small adaptations regarding the parametrization and the parameters involved. The design is divided into two stages that need to arrange the methodology at different levels throughout the workflow. For this reason, it is mandatory to adapt inputs to the algorithm according to the goal. The result is a skyscraper placed in the financial district of Hong Kong, which has both the features of a Grade A Office building and can mitigate the UHI effect thanks to its particular and optimized shape.
keywords	shape optimization; Computational design; Genetic Algorithm; UHI effect; ventilation
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2021_111
id	caadria2021_111
authors	Gautama, Jennifer, Yogiaman, Christine and Tracy, Kenneth
year	2021
title	Future Coastal Cities with Biorock Infrastructure - Alternative Coastal Futures with Biodesign
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 483-492
doi	https://doi.org/10.52842/conf.caadria.2021.1.483
summary	Despite having the potential of being a durable building material, Biorock, a form of calcium carbonate formed by the electro-accumulation of minerals dissolved in seawater, has never been applied on an architectural level due to its slow accretion process. This paper aims to plays out the possible narrative of this slow accruing material process in the incrementally submerged coastline of Jakarta, to empower local marginalized communities to self-construct a new city for habitation using Biorock, especially where building material resources may be limited. Urban cores with basic communal, housing and aquaculture facilities will be established using Biorock as the main building structure, which would be harvested in response to the gradual sea level rise.
keywords	Biorock; Accretion; Aggregation; Coastal Floods; Biodesign
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2021_311
id	caadria2021_311
authors	Gu, Xiangshu, Tian, Shulin, Zhang, Baihui, Tong, Ziyu and Gan, Jingwen
year	2021
title	SECTIONMATRIX - Mapping Urban Form through Urban Sections
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 599-608
doi	https://doi.org/10.52842/conf.caadria.2021.2.599
summary	Most of the traditional studies on urban morphology are based on aerial views. However, the 2D plane model fails to describe the height information of buildings and the relation of buildings and the urban external space. An urban section is another map of an urban area. Through a series of continuous vertical urban slices, the city texture can be transformed into planar linear information containing height and width information. This paper proposes several indicators to describe a series of urban section slices and uses a three-dimensional coordinate mapping method Sectionmatrix to quantify and analyze the relation between the physical geometrical indicators and urban form from the section perspective. Through the case analysis of multiple residential blocks in Nanjing, China, the results showed that Sectionmatrix is convenient and efficient. Sectionmatrix relates the geometrical properties to the spatial characteristics of urban areas and provides a new way to classify, map and define building typologies. This new classification method reveals the tortuosity and complexity of residential blocks. By bridging the gap between quantity and form, the research also suggests other possible applications of Sectionmatrix as a control instrument and test framework for entire cities planning and design.
keywords	Urban Morphology; Urban Section; Sectionmatrix; Quantitative Analysis
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2021_148
id	caadria2021_148
authors	Hou, Yuhan and Loh, Paul
year	2021
title	Towards Swarm Construction
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 673-682
doi	https://doi.org/10.52842/conf.caadria.2021.1.673
summary	Swarm intelligence has primarily been explored in architecture as a form-finding technique with resulting material articulation using advanced 3d-printing technology. Researchers in engineering have developed swarm robotics for construction and fabrication, typically constraints to small scale prototypes as the technology matures within the field. However, a few research explores the implication of swarm robotics for construction on the building or urban scale. This paper presents a novel swarm robotics construction method using mole-like digging technology to construct new architectural language using machine intelligence. The research discusses the role of swarm intelligence behaviours in design and synthesis such behaviour with machine logics. The paper addresses the conference theme through the speculative projection of future construction methodology and reflects on how automation can impact the future of construct and design.
keywords	Swarm; Digital Fabrication; Robotic
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ascaad2021_114
id	ascaad2021_114
authors	Houda, Maryam
year	2021
title	Materiality: Linking a Digital Material Framework with the Anthropological Hand
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 568-580
summary	While computers and digital technology have evolved over the years and are changing the way we design and construct, some have criticized the way in which human tactility and intuition with material has diminished at the cost of increasing productivity and efficiency. Although the digital culture that architecture is engaged with today has brought about complex forms that could not have been possible by hand, there is a rising question of the place of craft and a hand-brain coordination in design, and the notion of learning through making. This paper explores the benefits and limitations of digital design tools in light of physically exploring building materials and gaining tactile intuition. While digital tools investigate structural optimisation methods using a parametric design workflow, physical experiments deal with understanding the transitional state of mud and its dynamic properties. This research is interested in how information is learnt from materiality during the physical act of making and what tactile experimentation can offer that the digital space cannot. Three key areas are explored: geometry and parametric variation, material properties and morphogenic behavior, as well as structural optimization methods using density grids. Force-matter relations are investigated through exploring material parameters through digital and physical form-finding processes as a way of exploring the notion of re-introducing the hand and craft in the design process which may bring about novel ways of thinking and doing.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2021_354
id	caadria2021_354
authors	Huang, Chenyu, Gong, Pixin, Ding, Rui, Qu, Shuyu and Yang, Xin
year	2021
title	Comprehensive analysis of the vitality of urban central activities zone based on multi-source data - Case studies of Lujiazui and other sub-districts in Shanghai CAZ
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 549-558
doi	https://doi.org/10.52842/conf.caadria.2021.2.549
summary	With the use of the concept Central Activities Zone in the Shanghai City Master Plan (2017-2035) to replace the traditional concept of Central Business District, core areas such as Shanghai Lujiazui will be given more connotations in the future construction and development. In the context of todays continuous urbanization and high-speed capital flow, how to identify the development status and vitality characteristics is a prerequisite for creating a high-quality Central Activities Zone. Taking Shanghai Lujiazui sub-district etc. as an example, the vitality value of weekday and weekend as well as 19 indexes including density of functional facilities and building morphology is quantified by obtaining multi-source big data. Meanwhile, the correlation between various indexes and the vitality characteristics of the Central Activities Zone are tried to summarize in this paper. Finally, a neural network regression model is built to bridge the design scheme and vitality values to realize the prediction of the vitality of the Central Activities Zone. The data analysis method proposed in this paper is versatile and efficient, and can be well integrated into the urban big data platform and the City Information Modeling, and provides reliable reference suggestions for the real-time evaluation of future urban construction.
keywords	multi-source big data; Central Activities Zone; Vitality; Lujiazui
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 377-386
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_074
id	caadria2021_074
authors	Song, Yanan, Li, Keke, Lin, Yuqiong and Yuan, Philip F.
year	2021
title	Research on Self-Formation Wind Tunnel Platform Design based on dynamic gridding mechanical devices
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 669-678
doi	https://doi.org/10.52842/conf.caadria.2021.2.669
summary	Nowadays, climate problems, such as urban ventilation, heat island effect are becoming increasingly serious. Performance-oriented buildings that respond positively to the environment are constructing a sustainable future of the living environment. This research introduces an autonomous Self-Formation Wind Tunnel (SFWT) platform based on 120 dynamic grid mechanical devices, and its building cluster morphology generation workflow in the conceptual design stage, for the rapid and mass formation experiments. The Self-formation wind tunnel plat-form, which has the advantages of both perceptive and real-time data, is able to use the techniques of machine learning to provide a new design paradigm, from environmental performance to physical morphology.
keywords	Self-Formation Wind Tunnel; Building Cluster Morphology; Dynamic Models; Mechanical Grid Devices; Environment Performance Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2021_194
id	caadria2021_194
authors	Sun, Chengyu, Li, MengTing and Jiang, Hanchen
year	2021
title	Developing an Automatic Code Checking System for the Urban Planning Bureau of Huangpu District in Shanghai
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 291-300
doi	https://doi.org/10.52842/conf.caadria.2021.1.291
summary	As Chinese cities entering a so-called organic renewal era, building projects runs with much more constraints from high-density and high-rise surroundings. Such a situation makes the technical review in any urban planning bureau time-consuming and error-prone, which conflicts with the developers profits and citizens rights. This study introduces a preliminary system being developed for the planning bureau of Huangpu District, Shanghai. It has covered 21 code items among 44 computational ones of the local planning codes last year, which automatically generates technical reviews upon developers submissions. Due to the feasible level of BIM application in domestic projects, a set of strategic approaches, such as the standardization of CAD drawings and the reconstruction of an internal building information model, are adopted rather than developing the system on any BIM platform directly. Two examples of technical reviews about distance-checking between buildings and length-checking of facades are demonstrated, in which officers reached confidential judgments in seconds rather than several days conventionally.
keywords	Planning Constraints; Code Checking; 3D Reconstruction; Design Automation; Building Information Model
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia21_182
id	acadia21_182
authors	Yang, Qi; Cruz-Garza, Jesus G.; Kalantari, Saleh
year	2021
title	MindSculpt
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 182-193.
doi	https://doi.org/10.52842/conf.acadia.2021.182
summary	MindSculpt enables users to generate a wide range of hybrid geometries in Grasshopper in real-time simply by thinking about those geometries. This design tool combines a non-invasive brain–computer interface (BCI) with the parametric design platform Grasshopper, creating an intuitive design workflow that shortens the latency between ideation and implementation compared to traditional computer-aided design tools based on mouse-and-keyboard paradigms. The project arises from transdisciplinary research between neuroscience and architecture, with the goal of building a cyber-human collaborative tool that is capable of leveraging the complex and fluid nature of thinking in the design process. MindSculpt applies a supervised machine-learning approach, based on the support vector machine model (SVM), to identify patterns of brain-waves that occur in EEG data when participants mentally rotate four different solid geometries. The researchers tested MindSculpt with participants who had no prior experience in design, and found that the tool was enjoyable to use and could contribute to design ideation and artistic endeavors.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_375
id	caadria2021_375
authors	Özlem Çavuş , Hizir Gökhan Uyduran , Delara Razzaghmanesh and Imdat As
year	2021
title	An evolutionary approach for topology finding in flexible and modular housing
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 231-240
doi	https://doi.org/10.52842/conf.caadria.2021.1.231
summary	Today, the living environment is much more complex due to rapid urbanization and cities hardly can bear increasing crowds. This evolving environment together with the change in living habits, put a strain on the shoulders of architects and engineers to find faster and more effective solutions towards flexible and responsive design in future city scenarios. Modular design is one of the most suitable solutions since it is based on interchangeable components that facilitate different combinations and activities responding to emerging needs and demands without demolishing a whole edifice. There are many available algorithms defining rules for the automated generation of modular building units but mainly designed for top-down solutions. This paper proposes an evolutionary approach aiming to find topological relations among the units based on a specific architectural program concerning environmental performance. Environmental conditions define the rules for the growth of units on site. The algorithm produces an automatic layout through a set of positioning rules for units organized around a core depending on a branching system. In this sense, this paper contributes to showing how rule-based modular growth on-site is shaped with environmental and architectural concerns for future city scenarios.
keywords	Modular Housing; Affordable Housing; Future City; Branching Structure; Evolutionary Approach
series	CAADRIA
type	normal paper
email
last changed	2022/06/07 07:57

_id	cdrf2021_92
id	cdrf2021_92
authors	Ana Zimbarg
year	2021
title	Bio-Design Intelligence
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_9
summary	Architecture has a substantial influence worldwide as it shapes our cities, and it is made to last. Urban areas are also responsible for 70% of the world’s carbon emissions. Consequently, architects are responsible for minimising the destructive effects of construction on the environment. How can biological intelligence be inserted in architecture as a possibility to increase environmental performance? Bio-design goes further than biology-inspired approaches. Biodesign refers to incorporating living organisms as an essential component of a system, changing the natural and built environment boundaries. It contains living and machine intelligence, whether embedded in the design process or in the building itself. This paper seeks to give an overview of bio-design and how it can be seen as a strategy of thinking of new research pathways.
series	cdrf
email
last changed	2022/09/29 07:53

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 160-169.
doi	https://doi.org/10.52842/conf.acadia.2021.160
summary	This research aims to explore the quantitative relationship between urban planning decisions and the health status of residents. By modeling the Point of Interest (POI) data and the geographic distribution of health-related outcomes, the research explores the critical factors in urban planning that could influence the health status of residents. It also informs decision-making regarding a healthier built environment and opens up possibilities for other data-driven methods. The data source constitutes two data sets, the POI data from OpenStreetMap, and the PLACES: Local Data for Better Health dataset from CDC. After the data is collected and joined spatially, a machine learning method is used to select the most critical urban features in predicting the health outcomes of residents. Several machine learning models are trained and compared. With the chosen model, the prediction is evaluated on the test dataset and mapped geographically. The relations between factors are explored and interpreted. Finally, to understand the implications for urban design, the impact of modified POI data on the prediction of residents' health status is calculated and compared. This research proves the possibility of predicting resident's health from urban conditions with machine learning methods. The result verifies existing healthy urban design theories from a different perspective. This approach shows vast potential that data could in future assist decision-making to achieve a healthier built environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_391
id	caadria2021_391
authors	Elshani, Diellza, Koenig, Reinhard, Duering, Serjoscha, Schneider, Sven and Chronis, Angelos
year	2021
title	Measuring Sustainability and Urban Data Operationalization - An integrated computational framework to evaluate and interpret the performance of the urban form.
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 407-416
doi	https://doi.org/10.52842/conf.caadria.2021.2.407
summary	With rapid urbanization, the necessity for sustainable development has skyrocketed, and sustainable urban development is a must. Recent advances in computing performance of urban layouts in real-time allow for new paradigms of performance-driven design. As beneficial as utilizing multiple layers of urban data may be, it can also create a challenge in interpreting and operationalizing data. This paper presents an integrated computational framework to measure sustainability, operationalize and interpret the urban forms performance data using generative design methods, novel performance simulations, and machine learning predictions. The performance data is clustered into three pillars of sustainability: social, environmental, and economical, and it is followed with the performance space exploration, which assists in extracting knowledge and actionable rules of thumb. A significant advantage of the framework is that it can be used as a discussion table in participatory planning processes since it could be easily adapted to interactive environments.
keywords	generative design; data interpretation ; urban sustainability; performance simulation; machine learning
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia21_112
id	acadia21_112
authors	Kahraman, Ridvan; Zechmeister, Christoph; Dong, Zhetao; Oguz, Ozgur S.; Drachenberg, Kurt; Menges, Achim; Rinderspacher, Katja
year	2021
title	Augmenting Design
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 112-121.
doi	https://doi.org/10.52842/conf.acadia.2021.112
summary	In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_135
id	caadria2021_135
authors	Mo, Yichen, Li, Biao, Wu, Jiaqian and Tang, Peng
year	2021
title	Archibase:A City-Scale Spatial Database for Architectural Research
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 519-528
doi	https://doi.org/10.52842/conf.caadria.2021.2.519
summary	The explosion of geolocation data and data-based algorithms has the potential to analyze sophisticated urban areas and foster a more robust urban model. To better collect and organize the city data, this paper introduces a city-scale spatial database called ArchiBase, built upon Java and web APIs of open source databases. With hierarchical, layered, and regularly-updated spatial data defined by relation table, ArchiBase allows indexing and geometric searching of the entire city and supports applications and extensions for different cities. This research is from a graduate urban design course aiming to renew Prato, an industrial city in Italy. ArchiBase first creates the base version of Prato from multiple data sources, then illustrates the usability and expandability through three simple applications. The use of ArchiBase can better interpret future cities and demonstrate the unparalleled opportunities of collaboration and remote work for urban researchers and designers.
keywords	Spatial Database; Data Model; Urban Design; Design Support Tools
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	cdrf2021_139
id	cdrf2021_139
authors	Shicong Cao1 and Hao Zheng
year	2021
title	A POI-Based Machine Learning Method for Predicting Residents’ Health Status
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_13
summary	Health environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field.
series	cdrf
email
last changed	2022/09/29 07:53

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