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	caadria2018_082
id	caadria2018_082
authors	Zhu, Li and Yang, Yang
year	2018
title	Optimization Design Study of Lightweight Temporary Building Integrated with PCMS Through CFD Simulation
doi	https://doi.org/10.52842/conf.caadria.2018.2.155
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 155-164
summary	In fact, the phase change materials (PCMs) integrated in the building envelope structure can decrease the buildings' energy consumption by enhancing thermal energy storage capacity, which has been acknowledged and appreciated by many engineers and architects. To achieve a better practical application effect under the minimum cost principle and provide a different design method based on indoor thermal discomfort evaluation results for stakeholders, this paper numerically test the application effect of composite envelope under Tianjin climate through commercial computational fluid dynamic soft (Fluent). Further, parameter sensitivity to thermal performance of the composite envelope and indoor thermal discomfort are investigated in this paper, and two different evaluation indicators are introduced and used here. The numerical results obtained in this paper support the high potential of using PCM in lightweight temporary buildings and highlight the further optimization design work.
keywords	Optimization design; Lightweight temporary building; PCMs; CFD simulation
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaaderis2023_11
id	ecaaderis2023_11
authors	Sepúlveda, Abel, Eslamirad, Nasim, Seyed Salehi, Seyed Shahabaldin, Thalfeldt, Martin and De Luca, Francesco
year	2023
title	Machine Learning-based Optimization Design Workflow based on Obstruction Angles for Building Facades
source	De Luca, F, Lykouras, I and Wurzer, G (eds.), Proceedings of the 9th eCAADe Regional International Symposium, TalTech, 15 - 16 June 2023, pp. 15–24
summary	This paper proposes a ML-based optimization design workflow based on obstruction angles for the optimization of building facades (i.e. g-value and window width). The optimization output consists of the optimal clustering of windows in order to ensure a desired level of daylight provision according to method 2 defined in the EN17307:2018 (i.e. based on Spatial Daylight Autonomy: sDA) and to not exceed a maximum level of specific cooling capacity (SCC). The independent variables or design parameters of the parametric model are: room orientation/dimensions, window dimensions, and obstruction angle (??). The ML prediction models were trained and tested with reliable simulation results using validate softwares. The total number of room combinations is 61440 for sDA and SCC simulations. The development of reliable (90% of right predictions) ML predictive models based on decision tree technique were calibrated. The optimal clustering of windows was done first by floors and secondly by the designer’s need to homogenize the external facade with similar glazing properties and window sizes, having impact on the annual heating consumption. The proposed method help designers to make accurate and faster design decisions during early design stages and renovation plans.
keywords	optimization, daylight, thermal comfort, cooling capacity, machine-learning predictive model, office buildings, cold climates
series	eCAADe
email
last changed	2024/02/05 14:28

_id	caadria2018_054
id	caadria2018_054
authors	Shen, Xiaofei
year	2018
title	Environmental Parametric Multi-Objective Optimization for High Performance Facade Design
doi	https://doi.org/10.52842/conf.caadria.2018.2.103
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 103-112
summary	This paper demonstrates the applicability of a data-integrated and user-friendly Multi-Objective Optimization (MOO) method within the Grasshopper (GH) parametric design interface which supports early stage design decision making for High Performance Building (HPB) façade. With multiple environmental objectives optimized and multiple geometric parameters adjusted in the same intuitive design space, designers with limited knowledge on scripting could easily set up the nodes simultaneously when the design is carried out to achieve the efficiency in HPB design optimization. An experiment utilizing the method, with DIVA as the environmental simulator and Octopus as the MOO solver, is demonstrated for rational daylight distribution, balanced solar heat gain and reduced energy use intensity. The findings show both potentials and limitations of the proposed method.
keywords	Multi-Objective Optimization; Environmental Parametrics; Generative Design; High Performance Facade
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2018_037
id	caadria2018_037
authors	Valitabar, Mahdi, Moghimi, Mahdi, Mahdavinejad, Mohammadjavad and Pilechiha, Peiman
year	2018
title	Design Optimum Responsive Façade Based on Visual Comfort and Energy Performance
doi	https://doi.org/10.52842/conf.caadria.2018.2.093
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 93-102
summary	Responsive Facades duo to continuously changes in climate conditions have an important role in reducing energy usage of buildings while providing higher level of visual comfort. This paper is a comparative study of responsive facades in a virtual format. Honeybee and Ladybug software were used for modeling and evaluating visual comfort as well as calculation of the energy consumption in a 3D model. It's a plug-in for grasshopper. This article's problem includes tow visual comfort criteria, DGP and illuminance. Various types of vertical and horizontal responsive facades were compared with a new form to achieve the optimal responsive façade. The results of research imply that with a few changes in secondary skin the new concept could slash energy use like common responsive facades while providing higher level of visual comfort. The important distinguishing point is the new concept from the same sample of responsive facades that is designed to pay more attention to the occupants' view connection with outside.
keywords	Responsive Facades; Architectural Design optimization; Visual comfort; Energy consumption
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaaderis2018_112
id	ecaaderis2018_112
authors	Kontovourkis, Odysseas and Konatzii, Panagiota
year	2018
title	Design-static analysis and environmental assessment investigation based on a kinetic formwork-driven by digital fabrication principles
source	Odysseas Kontovourkis (ed.), Sustainable Computational Workflows [6th eCAADe Regional International Workshop Proceedings / ISBN 9789491207143], Department of Architecture, University of Cyprus, Nicosia, Cyprus, 24-25 May 2018, pp. 131-140
keywords	This research focuses on design-static analysis and environmental assessment procedures that are based on the idea of a flexible kinetic formwork used as the automated mechanism for the production of bricks for porous wall structures. A key aspect of this investigation is the Life Cycle Assessment (LCA) analysis study that is applied in order to achieve, in parallel with the automated procedure, the sustainable potential of the products. For this purpose, the design and construction flexibility of the product is taken into account from the early design decision making stage by examining different sizes of bricks under fabrication including massive or porous ones in order to test their design and static performance, aiming to adapt their shape in multiple functional and environmental scenarios. In parallel, the LCA impact of the given design scenarios are taken into consideration, again from the early design phase, and include, among other objectives, material minimization, less environmental impact of building materials and less energy consumption based on the proposed digital fabrication technology. This is examined by comparing digital design and robotic automated results using three types of ecological materials.
series	eCAADe
email
last changed	2018/05/29 14:33

_id	ijac201816403
id	ijac201816403
authors	Pantazis, Evangelos and David Gerber
year	2018
title	A framework for generating and evaluating façade designs using a multi-agent system approach
source	International Journal of Architectural Computing vol. 16 - no. 4, 248-270
summary	Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.
keywords	Generative design, multi-agent systems, façade design, agent-based modeling, stochastic search
series	journal
email
last changed	2019/08/07 14:04

_id	acadia18_176
id	acadia18_176
authors	Bidgoli, Ardavan; Veloso,Pedro
year	2018
title	DeepCloud. The Application of a Data-driven, Generative Model in Design
doi	https://doi.org/10.52842/conf.acadia.2018.176
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 176-185
summary	Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.
keywords	full paper, design tools software computing + gaming, ai & machine learning, generative design, autoencoders
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_id	ecaade2018_268
id	ecaade2018_268
authors	Cheang, Jeremy Jenn Ren and Loh, Paul
year	2018
title	FOAM - Custom Single Task Construction Robot
doi	https://doi.org/10.52842/conf.ecaade.2018.1.157
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 157-164
summary	This paper discusses the design and fabrication of a novel in-situ fabrication system for building cladding envelope. The construction industry has utilised automation in onsite construction for many decades. This research examines how through the automation process, different construction techniques can be combined to generate a new system that is both performance and design lead. Through abstracting generative effects through the design process, the results are feedback into the fabrication process to construct a more meaningful dialogue between form, material and fabrication procedure. Using electronic prototyping, the researchers tested the system through large-scale prototypes. The paper concludes by discussing the interaction between material and design. We examine how this is evident in the machine workflow. The article addresses the theme of the conference through examining a revision of tool in design that embodied research knowledge for a more sustainable environment.
keywords	Digital Fabrication, Design workflow, Automation
series	eCAADe
email
last changed	2022/06/07 07:55

_id	ecaade2018_301
id	ecaade2018_301
authors	Cocho-Bermejo, Ana, Birgonul, Zeynep and Navarro-Mateu, Diego
year	2018
title	Adaptive & Morphogenetic City Research Laboratory
doi	https://doi.org/10.52842/conf.ecaade.2018.2.659
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 659-668
summary	"Smart City" business model is guiding the development of future metropolises. Software industry sales to town halls for city management services efficiency improvement are, these days, a very pro?table business. Being the model decided by the industry, it can develop into a dangerous situation in which the basis of the new city design methodologies is decided by agents outside academia expertise. Drawing on complex science, social physics, urban economics, transportation theory, regional science and urban geography, the Lab is dedicated to the systematic analysis of, and theoretical speculation on, the recently coined "Science of Cities" discipline. On the research agenda there are questions arising from the synthesis of architecture, urban design, computer science and sociology. Collaboration with citizens through inclusion and empowerment, and, relationships "City-Data-Planner-Citizen" and "Citizen-Design-Science", configure Lab's methodology provoking a dynamic responsive process of design that is yet missing on the path towards the real responsive city.
keywords	Smart City; Morphogenetic Urban Design; Internet of Things; Building Information Modelling; Evolutionary Algorithms; Machine Learning & Artificial Intelligence
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2018_399
id	ecaade2018_399
authors	Cutellic, Pierre
year	2018
title	UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI
doi	https://doi.org/10.52842/conf.ecaade.2018.2.131
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 131-138
summary	This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops integrating fast human cognitive responses and its individual capitalisation through time in order to reflect towards the generation of design and architectural features.
keywords	Human Computer Interaction; Neurodesign; Generative Design; Design Computing and Cognition; Machine Learning
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2018_315
id	ecaade2018_315
authors	Koehler, Daniel, Abo Saleh, Sheghaf, Li, Hua, Ye, Chuwei, Zhou, Yaonaijia and Navasaityte, Rasa
year	2018
title	Mereologies - Combinatorial Design and the Description of Urban Form.
doi	https://doi.org/10.52842/conf.ecaade.2018.2.085
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 85-94
summary	This paper discusses the ability to apply machine learning to the combinatorial design-assembly at the scale of a building to urban form. Connecting the historical lines of discrete automata in computer science and formal studies in architecture this research contributes to the field of additive material assemblies, aggregative architecture and their possible upscaling to urban design. The following case studies are a preparation to apply deep-learning on the computational descriptions of urban form. Departing from the game Go as a testbed for the development of deep-learning applications, an equivalent platform can be designed for architectural assembly. By this, the form of a building is defined via the overlap between separate building parts. Building on part-relations, this research uses mereology as a term for a set of recursive assembly strategies, integrated into the design aspects of the building parts. The models developed by research by design are formally described and tested under a digital simulation environment. The shown case study shows the process of how to transform geometrical elements to architectural parts based merely on their compositional aspects either in horizontal or three-dimensional arrangements.
keywords	Urban Form; Discrete Automata ; Combinatorics; Part-Relations; Mereology; Aggregative Architecture
series	eCAADe
email
last changed	2022/06/07 07:51

_id	ecaade2018_108
id	ecaade2018_108
authors	Luo, Dan, Wang, Jingsong and Xu, Weiguo
year	2018
title	Applied Automatic Machine Learning Process for Material Computation
doi	https://doi.org/10.52842/conf.ecaade.2018.1.109
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 109-118
summary	Machine learning enables computers to learn without being explicitly programmed. This paper outlines state-of-the-art implementations of machine learning approaches to the study of physical material properties based on Elastomer we developed, which combines with robotic automation and image recognition to generate a computable material model for non-uniform linear Elastomer material. The development of the neural network includes a few preliminary experiments to confirm the feasibility and the influential parameters used to define the final RNN neural network, the study of the inputs and the quality of the testing samples influencing the accuracy of the output model, and the evaluation of the generated material model as well as the method itself. To conclude, this paper expands such methods to the possible architectural implications on other non-uniform materials, such as the performance of wood sheets with different grains and tensile material made from composite materials.
keywords	neural network; robotic; material computation; automation
series	eCAADe
email
last changed	2022/06/07 07:59

_id	caadria2018_057
id	caadria2018_057
authors	Nandavar, Anirudh, Petzold, Frank, Nassif, Jimmy and Schubert, Gerhard
year	2018
title	Interactive Virtual Reality Tool for BIM Based on IFC - Development of OpenBIM and Game Engine Based Layout Planning Tool - A Novel Concept to Integrate BIM and VR with Bi-Directional Data Exchange
doi	https://doi.org/10.52842/conf.caadria.2018.1.453
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 453-462
summary	With recent advancements in VR (Virtual Reality) technology in the past year, it has emerged as a new paradigm in visualization and immersive HMI (Human-machine Interface). On the other hand, in the past decades, BIM (Building Information Modelling) has emerged as the new standard of implementing construction projects and is quickly becoming a norm than just a co-ordination tool in the AEC industry.Visualization of the digital data in BIM plays an important role as it is the primary communication medium to the project participants, where VR can offer a new dimension of experiencing BIM and improving the collaboration of various stakeholders of a project. There are both open source and commercial solutions to extend visualization of a BIM project in VR, but so far, there are no complete solutions that offer a pure IFC format based solution, which makes the VR integration vendor neutral. This work endeavors to develop a concept for a vendor-neutral BIM-VR integration with bi-directional data exchange in order to extend VR as a collaboration tool than a mere visualization tool in the BIM ecosystem.
keywords	BIM; VR; IFC; Unity; BIM-VR integration; HMI
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2023_10
id	ecaade2023_10
authors	Sepúlveda, Abel, Eslamirad, Nasim and De Luca, Francesco
year	2023
title	Machine Learning Approach versus Prediction Formulas to Design Healthy Dwellings in a Cold Climate
doi	https://doi.org/10.52842/conf.ecaade.2023.2.359
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. 359–368
summary	This paper presents a study about the prediction accuracy of daylight provision and overheating levels in dwellings when considering different methods (machine learning vs prediction formulas), training, and validation data sets. An existing high-rise building located in Tallinn, Estonia was considered to compare the best ML predictive method with novel prediction formulas. The quantification of daylight provision was conducted according to the European daylight standard EN 17037:2018 (based on minimum Daylight Factor (minDF)) and overheating level in terms of the degree-hour (DH) metric included in local regulations. The features included in the dataset are the minDF and DH values related to different combinations of design parameters: window-to-floor ratio, level of obstruction, g-value, and visible transmittance of the glazing system. Different training and validation data sets were obtained from a main data set of 5120 minDF values and 40960 DH values obtained through simulation with Radiance and EnergyPlus, respectively. For each combination of training and validation dataset, the accuracy of the ML model was quantified and compared with the accuracy of the prediction formulas. According to our results, the ML model could provide more accurate minDF/DH predictions than by using the prediction formulas for the same design parameters. However, the amount of room combinations needed to train the machine-learning model is larger than for the calibration of the prediction formulas. The paper discuss in detail the method to use in practice, depending on time and accuracy concerns.
keywords	Optimization, Daylight, Thermal Comfort, Overheating, Machine Learning, Predictive Model, Dwellings, Cold Climates
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	ijac201816202
id	ijac201816202
authors	Tamke, Martin; Paul Nicholas and Mateusz Zwierzycki
year	2018
title	Machine learning for architectural design: Practices and infrastructure
source	International Journal of Architectural Computing vol. 16 - no. 2, 123-143
summary	In this article, we propose that new architectural design practices might be based on machine learning approaches to better leverage data-rich environments and workflows. Through reference to recent architectural research, we describe how the application of machine learning can occur throughout the design and fabrication process, to develop varied relations between design, performance and learning. The impact of machine learning on architectural practices with performance-based design and fabrication is assessed in two cases by the authors. We then summarise what we perceive as current limits to a more widespread application and conclude by providing an outlook and direction for future research for machine learning in architectural design practice.
keywords	Machine learning, robotic fabrication, design-integrated simulation, material behaviour, feedback, Complex Modelling
series	journal
email
last changed	2019/08/07 14:03

_id	acadia18_186
id	acadia18_186
authors	Yin, Hao; Guo, Zhe; Zhao, Yao; Yuan, Philip F.
year	2018
title	Behavior Visualization System Based on UWB Positioning Technology
doi	https://doi.org/10.52842/conf.acadia.2018.186
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 186-195
summary	This paper takes behavioral performance as a starting point and uses ultra-wideband (UWB) positioning technology and visualization methods to accurately collect and present in-place behavioral data so as to explore the behavioral characteristics of space users. In this process, we learned the observation, quantification, and presentation of behavioral data from the evolution of behavioral research. Secondly, after a comparative analysis of four types of indoor positioning technologies, we selected UWB-positioning technology and the JavaScript programming language as the development tools for a behavior visualization system. Next, we independently developed the behavior visualization system, which required a deep understanding of the working principle of UWB technology and the visualization method of the JavaScript programming language. Finally, the system was applied to an actual space, collecting and presenting users’ behavioral characteristics and habits in order to verify the applicability of the system in the field of behavioral research.
keywords	full paper, design tools, ai + machine learning, big data, behavioral performance + simulation
series	ACADIA
type	paper
email
last changed	2022/06/07 07:57

_id	acadia18_196
id	acadia18_196
authors	Zhang, Yan; Grignard; Aubuchon, Alexander; Lyons, Keven; Larson, Kent
year	2018
title	Machine Learning for Real-time Urban Metrics and Design Recommendations
doi	https://doi.org/10.52842/conf.acadia.2018.196
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 196-205
summary	Cities are growing, becoming more complex, and changing rapidly. Currently, community engagement for urban decision-making is often ineffective, uninformed, and only occurs in projects’ later stages. To facilitate a more collaborative and evidence-based urban decision- making process for both experts and non-experts, real-time feedback and optimized suggestions are essential. However, most of the current tools for urban planning are neither capable of performing complex simulations in real time nor of providing guidance for better urban performance. CityMatrix was introduced to address these challenges. Machine learning techniques were applied to achieve real-time prediction of multiple urban simulations, and thousands of city configurations were simulated. The simulation results were used to train a convolutional neural network (CNN) to predict the traffic and solar performance of unseen city configurations. The prediction with the CNN is thousands of times faster than the original simulations and maintains a high-quality representation of the results. This machine learning approach was applied as a versatile, quick, accurate, and computationally efficient method not only for real-time feedback, but also for optimized design recommendations. Users involved in the evaluation of this project had a better understanding of the embodied trade-offs of the city and achieved their goals in an efficient manner.
keywords	full paper, optimization, collaboration, urban design & analysis, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:57

_id	caadria2018_211
id	caadria2018_211
authors	Zhao, Yao, Guo, Zhe, Yin, Hao, Yao, Jiawei and Yuan, Philip F.
year	2018
title	Behavioral Data Analysis and Visualization System Base on UWB Interior Positioning Technology
doi	https://doi.org/10.52842/conf.caadria.2018.2.217
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 217-226
summary	The behavioral patterns of human in buildings influence the rational setting of space and function dramatically. However, due to the lack of data acquisition methods and data accuracy, big data analysis and visualization research in the microscopic aspects of indoor space is hampered. With the maturity of indoor positioning technology, UWB (Ultra Wideband) positioning technology based on narrow pulse has the characteristics of high transmission rate, low transmit power and strong penetrating ability, which provides more accurate results for the behavior data acquisition in indoor space. In this research, the big data thinking has been introduced into the behavioral performance analysis process. Therefore, data acquisition, data storage and management, behavioral data visualization and machine learning algorithms are integrated into a set of behavioral data analysis and visualization system, to quantitative research the behavioral characteristics of visitors in the exhibition hall by the on-site experiment .
keywords	UWB interior positioning technology; Behavior Data Visualization; on-site experiment
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2018_181
id	caadria2018_181
authors	Chun, Junho, Lee, Juhun and Park, Daekwon
year	2018
title	TOPO-JOINT - Topology Optimization Framework for 3D-Printed Building Joints
doi	https://doi.org/10.52842/conf.caadria.2018.1.205
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 205-214
summary	Joints and connectors are often the most complex element in building assemblies and systems. To ensure the performance of the assemblies and systems, it is critical to optimize the geometry and configurations of the joints based on key functional requirements (e.g., stiffness and thermal exchange). The proposed research focuses on developing a multi-objective topology optimization framework that can be utilized to design highly customized joints and connections for building applications. The optimized joints that often resemble tree structures or bones are fabricated using additive manufacturing techniques. This framework is built upon the integration of high-fidelity topology optimization algorithms, additive manufacturing, computer simulations and parametric design. Case studies and numerical applications are presented to demonstrate the validity and effectiveness of the proposed optimization and additive manufacturing framework. Optimal joint designs from a variety of architectural and structural design considerations, such as stiffness, thermal exchange, and vibration are discussed to provide an insightful interpretation of these interrelationships and their impact on joint performance.
keywords	Topology optimization; parametric design; 3d printing
series	CAADRIA
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last changed	2022/06/07 07:56

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