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	caadria2022_522
id	caadria2022_522
authors	Cheng, Sifan, Leung, Carson Ka Shut and van Ameijde, Jeroen
year	2022
title	Evaluating the Accessibility of Amenities toward Walkable Neighourhoods: an Integrated Method for Testing Alternatives in a Generative Urban Design Process
doi	https://doi.org/10.52842/conf.caadria.2022.1.495
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. 495-504
summary	Studies have shown that walkable communities reduce traffic-related pollution and the risk of chronic illnesses, promote economic growth and prosperity, and stimulate community participation and the growth of social capital. To assess the walkability of urban areas, various methodologies have been developed around shortest-distance calculations between various points of interest (POIs), yet their outcomes do not guide potential urban design improvements. The absence of appropriate measurements and procedures that may give quantitative and actionable feedback to support design decision-making is one of the primary issues in building walkable neighborhoods. The work presented in this paper revolves around a new workflow, that employed Urbano, a mobility simulation and assessment tool, and integrated it within a generative design process to allowing for the quantitative evaluation on amenity accessibility for several alternative design scenarios for a case study site in Mong Kok, Hong Kong. The results show how this data-driven urban design process benefits from generative techniques to produce solutions with improved contextual connectivity, energy-efficient urban form, and good quality public spaces that contribute to the walkability of neighbourhoods.
keywords	Generative Urban Design, Walkability, Urbano, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_102
id	caadria2022_102
authors	Gardner, Nicole, Haeusler, Matthias Hank, Yu, Daniel, Barton, Jack, Dunn, Kate and Huang, Tracy
year	2022
title	Revisiting Shoei Yoh: Developing a Workflow for a Browser-Based 3D Model Environment to Create an Immersive Digital Archive
doi	https://doi.org/10.52842/conf.caadria.2022.1.687
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. 687-696
summary	The digitisation of architecturally significant buildings and sites creates opportunities to innovate methods of analysis, interpretation, representation, and audience engagement. To illustrate this potential, but also examine the attendant challenges, this paper outlines a research project that has digitised archival assets and living buildings designed by the Japanese architect Shoei Yoh to create an immersive 3D Spatial Archive. It focuses particularly on the creation of a browser-based 3D environment using WebGL technology that connects to and displays a repository of digitised archival assets. This includes the use of 3D scan data of Yoh's Naiju Community Centre project to accurately model the 3D immersive environment and a Grasshopper / Rhino into the glTF. File format (graphics library Transmission Format) workflow to render Naiju‚s complex geometry and detailed outdoor scenery. The paper demonstrates how using the .glTF File, which is an open format specifically for transmitting processed and pre-calculated 3D models, can improve the processing efficiency of web-browser based 3D environments. Improving the stability and processing speed of 3D browser-based environments is significant to enhancing how audiences can connect with and experience culturally significant sites remotely. The digital recreation and repurposing of Naiju (which is currently unoccupied and in a state of disrepair) as an immersive archival exhibition space operates to simultaneously protect the real building from over visitation, but also raise awareness of its cultural significance to support preservation efforts. In so doing, the paper makes a further contribution to the developing field of digital cultural heritage.
keywords	Digital Cultural Heritage, Browser-based Modelling, glTF File, Architectural Visualisation, Shoei Yoh, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_196
id	caadria2022_196
authors	Grisiute, Ayda, Shi, Zhongming, Chadzynski, Arkadiusz, Silvennoinen, Heidi, von Richthofen, Aurel and Herthogs, Pieter
year	2022
title	Automated Semantic SWOT Analysis for City Planning Targets: Data-driven Solar Energy Potential Evaluations for Building Plots in Singapore
doi	https://doi.org/10.52842/conf.caadria.2022.1.555
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. 555-564
summary	Singapore‚s urban planning and management is cross-domain in nature and need to be assessed using multi-domain indicators ‚ such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation.
keywords	Semantic Web, Knowledge Graphs, SWOT analysis, energy-driven urban design, SDG 11, SDG 7
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_133
id	ecaade2022_133
authors	Grisiute, Ayda, Silvennoinen, Heidi, Li, Shiying, Chadzynski, Arkadiusz, von Richthofen, Aurel and Herthogs, Pieter
year	2022
title	Unlocking Urban Simulation Data with a Semantic City Planning System - Ontologically representing and integrating MATSim output data in a knowledge graph
doi	https://doi.org/10.52842/conf.ecaade.2022.2.257
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 257–266
summary	Simulation models generate an abundance of rich raw data that remains difficult to access for non-experts. However, such data could be unlocked and utilised with a Semantic City Planning System that improves data accessibility and transparency. This paper describes a process of ontologically representing mobility simulation output data using Semantic Web technologies and storing it in a dynamic geospatial knowledge graph. Our work presents two benefits: 1) formally representing simulation output data increases the accessibility and transparency of urban simulation models, and 2) access to under-utilised rich data unlocks novel cross-domain knowledge explorations and research possibilities. We demonstrate these benefits by means of cross-domain queries related to typical city planning questions.
keywords	Semantic Web Technology, Mobility, Urban Planning, Ontology, MATSim, Knowledge Graph
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220303
id	ijac202220303
authors	Kirdar, Gulce; Gulen Cagdas
year	2022
title	A decision support model to evaluate liveability in the context of urban vibrancy
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 528–552
summary	Liveability can be accepted as an umbrella term covering all the factors that make a place to live. We recognize the versatility of urban liveability and focus on the vibrancy aspect. Regarding the literature, we compile variables affecting urban liveability under the economic, image, and use value of place. This article aims to present a data-driven decision support system to evaluate different dimensions of vibrancy-focused liveability. We adopt a knowledge discovery process to handle the complexity of the liveability concept. This study develops a conditional-based relationship network of vibrancy parameters through the Bayesian Belief Network (BBN). Then, we assess the BBN’s correlations with statistics and causal relations with the survey in this study.These results mostly agree with the findings of the relevant literature. The economic value results show that the high density, diversity and accessibility add a premium to the land value of properties. The use value results also demonstrate that the diversity and density of activities, cultural attributes, and high accessibility support place attractiveness. The selected streetscape variables improve image value, except for building enclosure and condition. The study has the potential for urban planners to vitalize neighborhoods by considering urban activities and urban physical attributes
keywords	liveability, vibrancy, knowledge discovery process, big data, locative data, Bayesian belief network
series	journal
last changed	2024/04/17 14:29

_id	ijac202220407
id	ijac202220407
authors	Lacroix, Igor; Orkan Zeynel Güzelci; Gonçalo Furtado Lopes; José Pedro Sousa
year	2022
title	Connecting the Portuguese system of evolutive housing with building information modeling: From analogical to digital methods
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 4, pp. 801–816
summary	In Portugal, in the 1960s and 1970s, there was research concerning a system of the architectural design of housing for economically less favored populations, which related sociological information with analogical computational methods and culminated with its application in the Local Ambulatory Support Service (SAAL). This article presents the digitization process of these methods for the development of an architectural design system for social housing. The main goal is to improve methodological procedures for the original research and, specifically, to adapt them to computational design and modeling processes. To this end, this research transposed the aforementioned methodology into an algorithmic model that matches sociological information acquired from an online form with a database of social housing floor plan images to generate a building information modeling (BIM) directly from the selected image source. The result is an algorithmic model informed by sociological data linked with a BIM model to enable further rationalization of architectural design.
keywords	evolutive housing, social housing, local ambulatory support service, sociological survey, algorithmic modeling, building information modeling
series	journal
last changed	2024/04/17 14:30

_id	caadria2022_77
id	caadria2022_77
authors	Marschall, Max and Sepulveda, Pablo
year	2022
title	How to Prevent a Passive House from Overheating: An Industry Case Study Using Parametric Design to Propose Compliance Strategies
doi	https://doi.org/10.52842/conf.caadria.2022.2.639
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. 639-648
summary	The airtight, well-insulated building fabric of a Passive House can reduce operational energy consumption but can also present a risk of overheating during summer. PHPP, the Excel tool used to model Passive Houses, considers the whole building as a single thermal zone; a simplification that might be partly responsible for the tool‚s limited ability to predict overheating risk. The current study on a real-world project provides insights on two topics. First, we compare PHPP‚s overheating assessment with that of CIBSE‚s TM59 standard that requires dynamic energy modelling at a room level. Our results support the claim that PHPP underestimates overheating; in our case, glazing SHGC and air change rate were some of the most important parameters affecting compliance, as were some other, rarely analysed factors like ratio of external wall to room volume. Second, we report on the effectiveness of using parametric design for compliance modelling of this kind, and found that parameter studies, coupled with appropriate data visualisation, are an effective way to build intuition on a design problem of this kind.
keywords	Passive House, social housing, EnergyPlus modelling, PHPP modelling, overheating risk, parametric data visualisation, SDG 3, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_169
id	caadria2022_169
authors	Xu, Hang and Wang, Tsung-Hsien
year	2022
title	An Integrated Parametric Generation and Computational Workflow to Support Sustainable City Planning
doi	https://doi.org/10.52842/conf.caadria.2022.1.535
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. 535-544
summary	To examine how efforts in the built environment can contribute to global climate change mitigation at the urban scale, urban building energy modelling (UBEM) is one of the research areas gaining increasing interest in recent years. However, limited studies systematically illustrate a comprehensive UBEM workflow for most architects and urban planners considering available public datasets, particularly at the early conceptual design phase. In current UBEM studies, major challenges arise from the lack of fine-grained measured urban data and incompatibility between software. To address these challenges and support future sustainable cities and communities, this paper proposed a streamlined computational workflow of UBEM to facilitate sustainable urban design development. Through a case study of Sheffield in the UK, this paper demonstrated an automated and standardised computational workflow that can test the decarbonisation potential in built environments by evaluating energy demand and supply scenarios at an urban scale. This workflow is envisaged to be applicable at various scales of an urban region given an appropriate geographic information system (GIS) dataset.
keywords	Parametric Design Generation, Urban Sustainability, Urban Building Energy Modelling, Building Performance Simulation, Renewable Energy, Decarbonisation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_6
id	architectural_intelligence2022_6
authors	Achim Menges, Fabian Kannenberg & Christoph Zechmeister
year	2022
title	Computational co-design of fibrous architecture
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00004-x
source	Architectural Intelligence Journal
summary	Fibrous architecture constitutes an alternative approach to conventional building systems and established construction methods. It shows the potential to converge architectural concerns such as spatial expression and structural elegance, with urgently required resource effectiveness and material efficiency, in a genuinely computational approach. Fundamental characteristics of fibre composite are shared with fibre structures in the natural world, enabling the transfer of design principles and providing a vast repertoire of inspiration. Robotic fabrication based on coreless filament winding, a technique to deposit resin impregnated fibre filaments with only minimal formwork, as well as integrative computational design methods are imperative to the development of complex fibrous building systems. Two projects, the BUGA Fibre Pavilion as an example for long-span structures, and Maison Fibre as an example of multi-storey architecture, showcase the application of those techniques in an architectural context and highlight areas of further research opportunities. The highly interrelated aesthetic, structural and fabrication characteristics of fibre nets are difficult to understand and go beyond a designer’s comprehension and intuition. An AI powered, self-learning agent system aims to extend and thoroughly explore the design space of fibre structures to unlock the full design potential coreless filament winding offers. In order to ensure feedback between all relevant design and performance criteria and enable interdisciplinary convergence, these novel design methods are embedded in a larger co-design framework. It formalizes the interaction of involved interdisciplinary domains and allows for interactive collaboration based on a central data model, serving as a base for design optimisation and exploration. To further advance research on fibre composites in architecture, bio-based materials are considered, continuing the journey of discovery of fibrous architecture to fundamentally rethinking design and construction towards a novel, computational material culture in architecture.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ecaade2022_270
id	ecaade2022_270
authors	Akcay Kavakoglu, Aysegul, Almac, Bihter, Eser, Begum and Alacam, Sema
year	2022
title	AI Driven Creativity in Early Design Education - A pedagogical approach in the age of Industry 5.0
doi	https://doi.org/10.52842/conf.ecaade.2022.1.133
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 133–142
summary	This study presents a pedagogical experiment on the integration of AI into the project studio in the early stages of design education. The motivation of the study is to support creative encounters in design studios by promoting student-design representation, student-student, and student-artificial intelligence (AI) interaction. In the scope of this study, a short-term studio project is used as a case study to examine these creative encounters. The experiment covers five stages that enable a recursive analysis-synthesis action. The stages include (i) precedent analysis of a given set of building façades images, (ii) feature extraction, (iii) composing new façade representations through employing previously generated features, (iv) training an AI by the use of styleGAN2-ADA with the outcomes of stage 3, (v) Use of synthetically generated façade images as a design driver. The pedagogical experiment is evaluated through the lenses of novelty, style, surprisingness, and complexity concepts. The challenges and potentials are introduced, as well as elaborations on the future directions of the interplay between AI-oriented making and first-year student making.
keywords	Artificial Intelligence, Computational Creativity, Design Education, StyleGAN2-ADA
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_167
id	caadria2022_167
authors	Aman, Jayedi, Matisziw, Timothy C, Kim, Jong Bum and Luo, Dan
year	2022
title	Sensing the City: Leveraging Geotagged Social Media Posts and Street View Imagery to Model Urban Streetscapes Using Deep Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.595
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 595-604
summary	Understanding the relationships between individuals and the urban streetscape is an essential component of sustainable city planning. However, analysis of these relationships involves accounting for a complex mix of human behaviour, perception, as well as geospatial context. In this context, a comprehensive framework for predicting preferred streetscape characteristics utilizing deep learning and geospatial techniques is proposed. Geotagged social media posts and street view imagery are employed to account for individual sentiment and geospatial context. Natural Language Processing (NLP) and computer vision (CV) are then used to infer sentiment and model the visual environment within which individuals make posts to social media. An application of the developed framework is provided using Instagram posts and Google Street View imagery of the urban environment. A spatial analysis is conducted to assess the extent to which urban attributes correlate with the sentiment of social media postings. The results shed light on sustainable streetscape planning by focusing on the relationship between users and the built environment in a complex urban setting. Finally, limitations of the developed methodology as well as future directions are discussed.
keywords	Urban sustainability, data mining, pedestrian sentiments, transportation behavior, street level imagery, transformers, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_47
id	caadria2022_47
authors	An, Yudi
year	2022
title	Impact of Covid-19 on Associations between Land Use and Bike-Sharing Usage
doi	https://doi.org/10.52842/conf.caadria.2022.1.605
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. 605-614
summary	Bike-sharing as a human-centred, zero-emission, sustainable, alternative, and easily accessible transport mode has been implemented globally and consistently contributing to communities and the environment by alleviating consumption of natural sources, traffic congestion, and air pollution, which is considered a solution for future cities. The appearance of Covid-19 significantly impacts public transportation modes, including the bike-sharing system. The intention of this study was to investigate the spatiotemporal impact of the Covid-19 pandemic on associations between urban factors and bike-sharing usage in Los Angeles, United States, by analysing a sizeable actual trip dataset and employing geographically weighted regression (GWR) models. GWR was conducted for examining the varying spatial association between bike infrastructure, public transport, and urban land use factors, and bike-sharing trip volume. The results indicated that bike-sharing usage significantly decreased during the pandemic and essential service as restaurant was found consistently and positively associated with bike-sharing use. GWR provided clear spatial patterns of bike usage based on urban land use and big user databases. The outcomes of this study could inspire policymakers and shared mobility operators to support these safe, sustainable transport alters (such as rebalancing bike stations), help city resilience, and shape a sustainable future of mobility in the post-Covid-19 era.
keywords	Bike-Sharing, Covid-19, Land Use, Geographically Weighted Regression, Big Data, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_139
id	caadria2022_139
authors	Ataman, Cem, Tuncer, Bige and Perrault, Simon
year	2022
title	Asynchronous Digital Participation in Urban Design Processes: Qualitative Data Exploration and Analysis With Natural Language Processing
doi	https://doi.org/10.52842/conf.caadria.2022.1.383
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. 383-392
summary	This paper aims to improve the usability of qualitative urban big data sources by utilizing Natural Language Processing (NLP) as a promising AI-based technique. In this research, we designed a digital participation experiment by deploying an open-source and customizable asynchronous participation tool, "Consul Project‚, with 47 participants in the campus transformation process of the Singapore University of Technology and Design (SUTD). At the end of the data collection process with several debate topics and proposals, we analysed the qualitative data in entry scale, topic scale, and module scale. We investigated the impact of sentiment scores of each entry on the overall discussion and the sentiment scores of each introduction text on the ongoing discussions to trace the interaction and engagement. Furthermore, we used Latent Dirichlet Allocation (LDA) topic modelling to visualize the abstract topics that occurred in the participation experiment. The results revealed the links between different debates and proposals, which allow designers and decision makers to identify the most interacted arguments and engaging topics throughout participation processes. Eventually, this research presented the potentials of qualitative data while highlighting the necessity of adopting new methods and techniques, e.g., NLP, sentiment analysis, LDA topic modelling, to analyse and represent the collected qualitative data in asynchronous digital participation processes.
keywords	Urban Design, Digital Participation, Qualitative Urban Data, Natural Language Processing (NLP), Sentiment Analysis, LDA Topic Modelling, SDG 10, SDG 11.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_208
id	caadria2022_208
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Petzold, Frank, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	The What, Why, What-If and How-To for Designing Architecture, Explainability for Auto-Completion of Computer-Aided Architectural Design of Floor Plan Layouting During the Early Design Stages
doi	https://doi.org/10.52842/conf.caadria.2022.2.435
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. 435-444
summary	In the next thirty years, the world's population is expected to increase to ten billion people, posing major challenges for the construction industry. To meet the growing demands for residential housing in the future, architects need to work faster, more efficiently, and more sustainably, while increasing architectural quality. The hypothetical intelligent design assistant WHITE BRIDGE, based on the methods of the 'metis' projects, suggests further design steps to support the architectural design decision-making processes of the early design phases. This facilitates faster and better decisions early in the process for a more responsible resource consumption, better mental well-being, and ultimately economic growth. Through a case study we investigate if additional information supports the understanding of these suggestions to reduce the cognitive workload of architectural design decisions on the backdrop of their respective representation. The paper contributes an approach for visualising explanations of an intelligent design assistant, their integration into paper prototypes for case studies, and a workflow for data collection and analysis. The results suggest that the cognitive horizon of the architects is broadened by the explanations, while the visualisation methods significantly influence the usefulness and use of the conveyed information within the explanations.
keywords	Explainability, Artificial intelligence, XAI, SDG 3, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_32
id	sigradi2022_32
authors	Brasil, Alexander; Martinez, Andressa
year	2022
title	Social Housing Mass Customization: Description of a system for real-time cost and spatial generation
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 53–64
summary	This study explores mass customization as an alternative strategy for social housing provision. The paper aims to demonstrate the implementation of an integrated system based on the connection between Building Information Modeling and algorithmic-parametric modeling technologies, seeking to design variability with real-time cost and time data control of single-family housing units. We developed the study according to five phases: (1) context analysis and design language definition; (2) rule-based design system definition; (3) cost and execution time estimation; (4) computer system based on the specified technologies definition; (5) quantitative evaluation and qualitative evaluation of the system. The experiment demonstrates that with the aid of algorithmic-parametric modeling, building information manipulation and visualization can be responsive enough to meet mass demands.
keywords	Data analytics, Mass customization, Social Housing, BIM, Cost control
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ijac202220204
id	ijac202220204
authors	BuHamdan, Samer; Aladdin Alwisy; Thomas Danel; Ahmed Bouferguene; Zoubeir Lafhaj
year	2022
title	The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 216–237
summary	While the design practice in the architecture, engineering, and construction (AEC) industry continues to be acreative activity, approaching the design problem from a perspective of the decision-making science hasremarkable potentials that manifest in the delivery of high-performing sustainable structures. These possiblegains can be attributed to the myriad of decision-making tools and technologies that can be implemented toassist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom.Such combination comes to extreme importance amid the mounting pressure on the AEC industry players todeliver economic, environmentally friendly, and socially considerate structures. Despite the promisingpotentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary designendeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing andapplying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expediteidentifying viable design alternatives within the solutions space in multidisciplinary design while maximizingthe likelihood of finding the optimal design
keywords	Design evaluation, multidisciplinary design, reinforced learning, Markov Decision Process, social impact,architecture, engineering, and construction industry
series	journal
last changed	2024/04/17 14:29

_id	cdrf2022_396
id	cdrf2022_396
authors	Chengbi Duan, Suyi Shen, Dingwen Bao, and Xin Yan
year	2022
title	Exploration and Design of the Contemporary Bracket Set Through Topology Optimization
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_34
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Dou Gong, pronounced in Chinese, and known as Bracket Set, is a vital support component in the ancient wooden tectonic systems. It is located between the column and the beam and connects the eave and pillar, making the heavy roof extend out of the eaves longer. The development of the bracket set is entirely a microcosm of the development of ancient Chinese architecture; the aesthetic structure and oriental artistic temperament behind the bracket make it gradually become the cultural and spiritual symbol of traditional Chinese architecture. In the contemporary era, inheriting and developing the bracket set has become an essential issue. This paper introduces the topological optimization method bi-directional evolutionary structural optimization (BESO) for form-finding. Through analyzing the development trend of bracket set and mechanical structure, the authors integrate 2D and 3D optimization methods and apply the hybrid methods to form-finding. This research aims to design a new bracket set corresponding to “structural performance-based aesthetics.“ The workflow proposed in this paper is valuable for architrave and other traditional building components.
series	cdrf
email
last changed	2024/05/29 14:03

_id	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
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. 313-322
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 287–295
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_338
id	caadria2022_338
authors	Dias Guimaraes, Gabriela, Gu, Ning, Gomes da Silva, Vanessa, Ochoa Paniagua, Jorge, Rameezdeen, Rameez, Mayer, Wolfgang and Kim, Ki
year	2022
title	Data, Stakeholders, and Environmental Assessment: A BIM-Enabled Approach to Designing-out Construction and Demolition Waste
doi	https://doi.org/10.52842/conf.caadria.2022.2.587
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. 587-596
summary	Construction and Demolition waste has started to become a target in the path for a more sustainable industry mainly due to massive resource consumption, land depletion and emissions. As a substantial amount of waste originates due to inadequate decision-making during design, strategies to design-out waste are required. Accurate environmental impact of, not only the whole building, but construction materials and elements are crucial to the development of these strategies, but dependent on data availability, expert knowledge and proper sharing and storage of information. Hence, this study aims to investigate the relation between data, stakeholders and environmental assessment to properly build a design-out waste framework. An in-depth data collection from literature review and stakeholders' interviews guided the development of a conceptual framework to assist designers with information related to waste production and its reduction. After that, the necessary technical specifications for its adoption through a BIM environment were analysed. Its contribution is firstly on a shift of thinking during the design phase, as the goal is to provide environmental information so designers can take into consideration the long-term consequences of waste from different strategies and solutions; and secondly in the development of a computational tool that facilitates the design-out process.
keywords	Construction and Demolition Waste, Design, BIM, Environmental Data, Stakeholders, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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