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	caadria2021_399
id	caadria2021_399
authors	Alsalman, Osama, Erhan, Halil, Haas, Alyssa, Abuzuraiq, Ahmed M. and Zarei, Maryam
year	2021
title	Design Analytics and Data-Driven Collaboration in Evaluating Alternatives
doi	https://doi.org/10.52842/conf.caadria.2021.2.101
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. 101-110
summary	Evaluation of design ideas is an important task throughout the life cycle of design development in the AEC industry. It involves multiple stakeholders with diverse backgrounds and interests. However, there is limited computational support which through this collaboration is facilitated, in particular for projects that are complex. Current systems are either highly specialized for designers or configured for a particular purpose or design workflow overlooking other stakeholders' needs. We present our approach to motivating participatory and collaborative design decision-making on alternative solutions as early as possible in the design process. The main principle motivating our approach is giving the stakeholders the control over customizing the data presentation interfaces. We introduce our prototype system D-ART as a collection of customizable web interfaces supporting design data form and performance presentation, feedback input, design solutions comparisons, and feedback compiling and presentation. Finally, we started the evaluation of these interfaces through an expert evaluation process which generally reported positive results. Although the results are not conclusive, they hint towards the need for presenting and compiling feedback back to the designers which will be the main point of our future work.
keywords	Design Analytics; Collaboration; Visualizations
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_113
id	ascaad2021_113
authors	Gün, Ahmet; Burak Pak, Yüksel Demir
year	2021
title	Technology-Driven Participatory Spatial Design in a Developing World Context: The Case of Istanbul
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. 551-567
summary	Nowadays, ICT-based participatory design methods, techniques and tools are increasingly used across the globe. A majority of these are employed in high-income “developed” countries with advanced democratic systems which aim at including citizens; desires, needs, proposals as valuable input in city-making processes. In contrast, in the Global South, only a limited number of ICT-based practices aim to empower the citizens in urban design and planning at higher instances. There is a need for deeper research into how citizens can be involved in urban design in developing countries like Turkey situated in between the Global North and the South. In this context, this research will focus on Istanbul, Turkey as a key case. Different than the developed world context, enabling ICT-based participation in Turkey has a wide range of challenges. Among those are the lack of open and governmental data and transparency, the unwillingness of the policymakers to promote and employ participatory design, top-down approaches are the other weak points of these countries. Responding to these challenges, the aims of this study are: 1) to critically address the weaknesses and requirements of existing urban development practices in developing countries with a focus on Turkey, Istanbul and 2) to discuss the possible potentials of ICT-based participation tools and techniques to involve citizens in city-making processes.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ecaade2021_108
id	ecaade2021_108
authors	Romero, Rosaura Noemy Hernandez and Pak, Burak
year	2021
title	Understanding Design Justice in a Bottom-up Housing through Digital Actor-Network Mapping - The case of solidary mobile housing in Brussels
doi	https://doi.org/10.52842/conf.ecaade.2021.1.131
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 131-140
summary	This paper is a study of an ongoing housing project in Brussels (SMH) which involves bottom-up spatial occupation and 'making' by activists, activist architects, social workers and citizens. The particular focus of this paper is on the critical spatial agency of the citizens, activist-architects and artefacts for enabling architectural design justice (ADJ) in the SMH. Building on the Actor-Network Theory of Latour (2005) we developed an analytic method called Actor Link Mapping and Analysis (ALMA) which involves data collection from a wide range of network actors, the generation of a variety of digital network maps, making computational analysis, followed by workshops and interviews to discuss the findings. ALMA was used to recognize potential assets which are essential for design justice practices and networks. The analysis revealed the limits to community control of design processes and practices as well as limits to the conceptual links surrounding socio-spatial equality, thus limits to design justice in the SMH project. Our research also revealed a plethora of new roles and agencies in bottom-up housing production which were essential to understanding the dynamics and power distribution among the different actors.
keywords	Network Mapping; Network Analysis; Housing; Co-creation; Design Justice; Actor-Network Theory
series	eCAADe
email
last changed	2022/06/07 07:56

_id	acadia21_122
id	acadia21_122
authors	Velikov, Kathy; Hasan, Kazi Najeeb; del Campo, Matias; Xie, Ruxin; Denit, Lucas; Boyce, Brent
year	2021
title	Design Engine
doi	https://doi.org/10.52842/conf.acadia.2021.122
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. 122-133.
summary	Generative design offers the possibility to heuristically explore data-driven design iterations during the design process. This enables performance-informed feedback and the possibility for exploring viable options with stakeholders earlier in the design process. Since architectural design is a complex, nonlinear process that requires trade-offs and compromises among multiple requirements, many of which are in conflict with each other, a multi-objective solver provides a spectrum of possible solutions without converging on a single optimized individual. This enables a more informed design possibility space that is open to collaborative decision-making. This paper describes the development of a custom multi-objective generative design workflow to visualize families of possible future building typologies with a focus on the impact of site, form, envelope performance, and glazing. Three future design scenarios are generated for three urban U.S. locations projected to grow and where progressive environmental performance stretch codes have been adopted. Drivers such as plausible site, procurement, financing, value chain, and construction typology inform possibilities for built form, envelope technologies, and performance in relation to local codes, environment, and occupant health, are transformed into design inputs through urban, spatial and environmental simulation tools for a "building design generator," or a multi-objective optimizer tool that produces an array of possible building massing and schematic envelope design options. The paper concludes with pointing out some of the gaps in data of current evaluation tools, the need for interoperability across platforms, and this points to multiple trajectories of future research in this area.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2021_234
id	sigradi2021_234
authors	Al Nouri, Mhd Ziwar, Baghdadi, Bilal and Khateeb, Nairooz
year	2021
title	Re-coding Post-War Syria: The Role of Data Collection & Objective Investigations in PostWar Smart City
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 127–145
summary	Re-coding post-war Syria is an ongoing research and data platform, focused on innovation and collecting comprehensive, infrastructural and socioeconomic analytics, synchronization data, by using AI driven to give a more transparent image of innovating a new methodology to regenerate the future of post-war smart cities into advanced and sustainable urban environments in a smarter way (Fig. 1). The pressure to achieve a rapid Post-war smart city without clear strategy and comprehensive analysis of all aspects will cause a particularly catastrophic collapse in the interconnected social structure, services, education and health care system, leaving a long-term impact on the society. This paper presents the current status of the Research & Documentation methodology in the Data Collection phase by the objective investigations conducted through a series of local and international workshops species developed in this research called “Re-Coding“, offering consequent direct ground surveys, statistics and documentation study of the targeted areas, merging professionalism and youth power with local community to detect an open source data used as a tool to re-generate a precarious area towards a new methodology.
keywords	Post-War Smart cities, Collecting Data, Local community, Objective Investigations, Artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
doi	https://doi.org/10.52842/conf.acadia.2021.160
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.
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	cdrf2021_252
id	cdrf2021_252
authors	Chengyu Sun, Shuyang Li , Yinshan Lin, and Weilin Hu
year	2021
title	From Visual Behavior to Signage Design: A Wayfinding Experiment with Eye-Tracking in Satellite Terminal of PVG Airport
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_24
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Passengers principally rely on signage to making wayfinding decisions in transportation buildings. Most existing research focuses on the analysis of the wayfinding trajectory, but there is less attention on the process of how passengers make the wayfinding decision. So, it is hard to accurately locate the causes of the wrong wayfinding decision. Taking the Satellite Terminal of Shanghai Pudong International Airport (PVG Airport) as an example, we adopted the eye-tracking technology and recorded the eye-tracking data of passengers observing the signage and making wayfinding decisions. Then, we compared and analyzed the data, presenting it by data visualization. This study found the causes of passengers making wrong wayfinding decisions and the visual behavior of wayfinding: the reconfirmation behavior, the priority of attention, and the clockwise observation. Finally, corresponding suggestions for signage design optimization are put forward regarding some wayfinding decision points. As a result, the optimized signage system in the satellite terminal is welcomed by the passengers two months later according to monthly questionnaires.
series	cdrf
last changed	2022/09/29 07:53

_id	caadria2021_089
id	caadria2021_089
authors	Cristie, Verina, Ibrahim, Nazim and Joyce, Sam Conrad
year	2021
title	Capturing and Evaluating Parametric Design Exploration in a Collaborative Environment - A study case of versioning for parametric design
doi	https://doi.org/10.52842/conf.caadria.2021.2.131
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. 131-140
summary	Although parametric modelling and digital design tools have become ubiquitous in digital design, there is a limited understanding of how designers apply them in their design processes (Yu et al., 2014). This paper looks at the use of GHShot versioning tool developed by the authors (Cristie & Joyce, 2018; 2019) used to capture and track changes and progression of parametric models to understand early-stage design exploration and collaboration empirically. We introduce both development history graph-based metrics (macro-process) and parametric model and geometry change metric (micro-process) as frameworks to explore and understand the captured progression data. These metrics, applied to data collected from three cohorts of classroom collaborative design exercises, exhibited students' distinct modification patterns such as major and complex creation processes or minor parameter explorations. Finally, with the metrics' applicability as an objective language to describe the (collaborative) design process, we recommend using versioning for more data-driven insight into parametric design exploration processes.
keywords	Design exploration; parametric design; history recording; version control; collaborative design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2021_113
id	caadria2021_113
authors	Fink, Theresa, Vuckovic, Milena and Petkova, Asya
year	2021
title	KPI-Driven Parametric Design of Urban Systems
doi	https://doi.org/10.52842/conf.caadria.2021.2.579
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. 579-588
summary	We present a framework for data-driven algorithmic generation and post-evaluation of alternative urban developments. These urban developments are framed by a strategic placement of diverse urban typologies whose spatial configurations follow design recommendations outlined in existing building and zoning regulations. By using specific rule-based generative algorithms, different spatial arrangements of these urban typologies, forming building blocks, are derived and visualized, given the aforementioned spatial, legal, and functional regulations. Once the envisioned urban configurations are generated, these are evaluated based on a number of aspects pertaining to spatial, economic, and thermal (environmental) dimensions, which are understood as the key performance indicators (KPIs) selected for informed ranking and evaluation. To facilitate the analysis and data-driven ranking of derived numeric KPIs, we deployed a diverse set of analytical techniques (e.g., conditional selection, regression models) enriched with visual interactive mechanisms, otherwise known as the Visual Analytics (VA) approach. The proposed approach has been tested on a case study district in the city of Vienna, Austria, offering real-world design solutions and assessments.
keywords	Urban design evaluation; parametric modelling; urban simulation; environmental performance; visual analytics
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ijac202119404
id	ijac202119404
authors	Ghandi, Mona; Blaisdell, Marcus; Ismail, Mohamed
year	2021
title	Embodied empathy: Using affective computing to incarnate human emotion and cognition in architecture
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 532–552
summary	This research aims to develop a cyber-physical adaptive architectural space capable of real-time responses topeople’s emotions, based on biological and neurological data. To achieve this goal, we integrated artificialintelligence (AI), wearable technology, sensory environments, and adaptive architecture to create anemotional bond between a space and its occupants and encourage affective emotional interactions betweenthe two. The project’s objectives were to (1) measure and analyze biological and neurological data to detectemotions, (2) map and illustrate that emotional data, and (3) link occupants’emotions and cognition to a builtenvironment through a real-time emotive feedback loop. Using an interactive installation as a case study, thiswork examines the cognition-emotion-space interaction through changes in volume, color, and light as ameans of emotional expression. It contributes to the current theory and practice of cyber-physical design andthe role AI plays, as well as the interaction of technology and empathy.
keywords	Places and awareness, artificial intelligence and machine learning in design, intelligent responsive spaces,affective computing in architecture, cognition-emotion-space interaction, embodied empathy, neuromorphicdesign, cyber-physical neurospaces
series	journal
email
last changed	2024/04/17 14:29

_id	sigradi2021_144
id	sigradi2021_144
authors	Kirdar, Gülce and Çagdaº, Gülen
year	2021
title	A Data-Driven Participatory Decision Model Proposal in the Context of Urban Vibrancy
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 175–189
summary	This paper focuses on the vibrancy of urban environments in the context of liveability. The objective is to explore the interrelationships of activity determinants what attracts visitors to support vibrancy decisions. The research questions how to uncover the relationships among vibrancy parameters and how to utilize the relation network for decision support. We utilize Bayesian Belief Network (BBN) for relational analysis and support with expert participation for an efficient decision-making. In this way, we expect to develop a causal by calibrating the data-driven BBN network. The results show that the user density has direct relationship with public open space, place rate, activity diversity, landmarks’ visitation rate cultural attributes, and accessibility; indirect relationship with time diversity, activity diversity and density. The results support the arguments on the importance of activity diversity in space and time, and intense urban pattern within the attractiveness public spaces for urbanity.
keywords	Big data, data-driven analysis, urban livability, participatory approach, design decision making.
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	caadria2021_191
id	caadria2021_191
authors	Shou, Xinyue, Chen, Pinyang and Zheng, Hao
year	2021
title	Predicting the Heat Map of Street Vendors from Pedestrian Flow through Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2021.2.569
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. 569-578
summary	Street vending is a recent policy advocated by city governments to support small and intermediate businesses in the post-pandemic period in China. Street vendors select their locations primarily based on their intuitions about the surrounding environment; they temporarily occupy popular locations that benefit their business. Taking the city of Chengdu as an example, this study aims to formulate the rules governing vendors location selection using machine learning and big data analysis techniques, thus identifying streets likely to become vital street markets. We propose a semantic segmentation method to construct heat maps that visualize and quantify the distribution of street vendors and pedestrians on public urban streets. The image-based generative adversarial network (GAN) is then trained to predict the vendors heat maps from the pedestrians heat map, finding the relationship between the locations of the vendors and the pedestrians. Our successful prediction of the vendors locations highlights machine learning techniques ability to quantify experience-based decision strategies. Moreover, suggesting potential marketing locations to vendors could help increase cities vitality.
keywords	Machine Learning; Big Data Analysis; Semantic Segmentation; Generative Adversarial Networks
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2021_243
id	caadria2021_243
authors	Stojanovic, Djordje and Vujovic, Milica
year	2021
title	Contactless and context-aware decision making for automated building access systems
doi	https://doi.org/10.52842/conf.caadria.2021.2.193
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. 193-202
summary	In the current context of the COVID-19 pandemic, contactless solutions are becoming increasingly important to making buildings more resilient to the spread of infectious diseases in complementing social distancing and disinfection procedures for disease prevention. The presented study focuses on contactless technology and its role beyond automated interaction with the built environment by examining how it expedited space use and could improve compliance with sanitary norms. We introduce a conceptual framework for the intelligent operation of automated doors in an educational facility, enabled by the network of sensory devices and the application of computational techniques. Our research indicates how versatile data gathered by RFID systems, in conjunction with data extracted from occupancy schedules and sanitary protocols, can be used to enable the intelligent and context-aware application of disease prevention measures. In conclusion, we discuss the benefits of the proposed concept and its role beyond the need for social distancing after the pandemic.
keywords	Human-Building Interaction; Interactive Environments; Responsive Environments; Occupancy Scheduling; Occupational Density
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2021_45
id	cdrf2021_45
authors	Wen Gao, Xuanming Zhang, Weixin Huang, and Shaohang Shi
year	2021
title	Command2Vec: Feature Learning of 3D Modeling Behavior Sequence—A Case Study on “Spiral-stair”
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_5
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	In this study, we applied machine learning to mine the event logs generated in modeling process for behavior sequence clustering. The motivation for the study is to develop cognitively intelligent 3D tools through process mining which has been a hot area in recent years. In this study, we develop a novel classification method Command2Vec to perceive, learn and classify different design behavior during 3D-modeling aided design process. The method is applied in a case study of 112 participate students on a ‘Spiral-stair’ modeling task. By extracting the event logs generated in each participate student’s modeling process into a new data structures: ‘command graph’, we classified participants’ behavior sequences from final 99 valid event logs into certain groups using our novel Command2Vec. To verify the effectiveness of our classification, we invited five experts with extensive modeling experience to grade the classification results. The final grading shows that our algorithm performs well in certain grouping of classification with significant features.
series	cdrf
email
last changed	2022/09/29 07:53

_id	caadria2021_063
id	caadria2021_063
authors	Zeng, Shaoting and Qiu, Song
year	2021
title	Parametric Design for Industrial Products - Taking Ergonomic Seat Design as an Example
doi	https://doi.org/10.52842/conf.caadria.2021.1.121
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. 121-130
summary	The main contents of this paper are the parametric design and its applications in industrial design, taking the ergonomic chair as the main design research carrier, conducting the experimental study, and explored the parametric industrial product design procedures and methods based on personalized design notion of Form follows behaviors. The research map focused on two fundamental parts of parametric design definition: the construction of the parameter relationship and the acquisition of parameters. The first part, through design space exploration (DSE), to translate the design problem into parameters relationship and variable ranges. The second part, using various software and hardware tools (Grasshopper, Arduino with pressure sensors, Kinect, etc.) to facilitate parameter acquisition and its application in the 10-person customer-driven experiments to the resulting design models and user datasets. Finally, through the formulation of quantitative evaluation for 110 sets of user data and models, selecting the best design solution for the 3D printed prototype, and conducting the user test.
keywords	Parametric industrial design; Ergonomic seat; Customer-driven design experiment; Posture sensing; Form follows behaviors
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2021_004
id	caadria2021_004
authors	Wei, Hu and Ke, Xiang
year	2021
title	Study of Measurement and Envi-met Simulation of Winter Night in NanPing Village under Wet and Cold Microclimate based on urban roughness
doi	https://doi.org/10.52842/conf.caadria.2021.2.427
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. 427-436
summary	This study selects four urban roughness parameters of building density, FAR, building dispersion ratio, and green rate to study the wet and cold microclimate in the winter night. According to the combination of 7 points measurement and 36 grids ENVI-met simulation, this study obtains microclimate research data. The significants of the winter night wet and cold microclimate is focused on improving the somatosensory temperature, and this study splits the target into two related directions, one is to extend the duration of comfortable temperature and humidity, another is to expand the comfortable area of temperature and humidity. By coupling analysis of urban roughness and the comfortable ratio, this study found out 11 relationship lines between urban roughness and nighttime microclimate in NanPing village. These laws offer the design strategy for NanPing Villages future development from three directions. These also provide a solution to achieve a low carbon, sustainable built environment.
keywords	Urban Roughness; Microclimate; Climate measurement; ENVI-met; Sustainable development
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2021_328
id	caadria2021_328
authors	Wells, Cameron, Schnabel, Marc Aurel, Moleta, Tane and Brown, Andre
year	2021
title	Beauty is in the Eye of the Beholder - Improving the Human-Computer Interface within VRAD by the active and two-way employment of our visual senses
doi	https://doi.org/10.52842/conf.caadria.2021.2.355
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. 355-364
summary	Whether it is via traditional methods with pen and paper or contemporary techniques such as 3D digital modelling and VR drawing, the eye typically plays a mostly passive or consuming role within the design process. By incorporating eye-tracking deeper within these methods, we can begin to discern this technologys possibilities as a method that encompasses the visual experience as an active input. Our research, however, developed the Eye-Tracking Voxel Environment Sculptor (EVES) that incorporates eye-tracking as there design actor. Through EVES we can extend eye-tracking as an active design medium. The eye-tracking data garnered from the designer within EVES is directly utilised as an input within a modelling environment to manipulate and sculpt voxels. In addition to modelling input, eye-tracking is also explored in its usability in the Virtual Reality User Interface. Eye-tracking is implemented within EVES to this extent to test the limits and possibilities of eye-tracking and the Human-Computer Interface within the realm of Virtual Reality Aided Design.
keywords	Human-Computer Interface (HCI); Eye-Tracking; Virtual Reality; modelling; sketching
series	CAADRIA
email
last changed	2022/06/07 07:58

_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	sigradi2021_283
id	sigradi2021_283
authors	Alexandrino, Joao Victor Mota, Amorim, Leonardo Edson, Muniz, Vinícius Fernandes and Leite, Raquel Magalhaes
year	2021
title	Architecture and Context: A Data-based Approach to Optimize Climate Performance of Built Facades
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 1139–1150
summary	The present research stems from a critical reflection about the environmental adaptability of existing building envelopes. The main goal is to explore how to balance environmental optimization with contextual constraints, using modularity, flexibility and mass customization as guiding principles. An application study was carried out with the development of a second skin proposal aligned with the use and context of the building under study. For this purpose, simulations that assess environmental conditions were developed within a visual programming tool, not only feeding the design process with essential information, but also providing a flexible creative process. Results show that such simulations allow the designer to interpret these studies more accurately, reducing the iterative guesswork, since in this workflow it is possible to transform these outputs into proposition parameters for new designs or interventions.
keywords	Data-Driven Analysis, Optimization, Parametric Facade Design, Thermal performance, High-low architecture, Mass Customization, Second Skin
series	SIGraDi
email
last changed	2022/05/23 12:11

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