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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Reformat results as: short short into frame detailed detailed into frame

_id	ecaade2020_334
id	ecaade2020_334
authors	Ntzoufras, Sotirios, Oungrinis, Konstantinos-Alketas, Liapi, Marianthi and Papamanolis, Antonios
year	2020
title	Robotic Swarms in Architectural Design - A communication platform bridging design analysis and robotic construction
doi	https://doi.org/10.52842/conf.ecaade.2020.2.453
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 453-462
summary	The research work fueling this paper examines ?ptimal approaches for bridging design analysis and robotic spatial construction. In this context, the paper presents the development of a unified platform for managing a swarm of robotic fabrication agents. The goal is the development of a streamlined methodology that guides the conversion of a design model into construction data code that can be assigned to the robotic swarm for fabrication.The work focuses on bridging architectural design platforms and distributed automation processes, on the one hand, and on the other, it targets the development of a functional management tool for adjusting and optimizing fabrication. A crucial parameter considered is the monitoring and assessment of all stages of the proposed process. This involves a constant exchange of information between the various actors, such as the swarm agents, the construction data and the designer - user. As a result, the construction process is treated as a constant reassessment and re-adjustment of the design parameters rather than the linear result of the original set of construction data. Therefore, the proposed system cannot be described as reactive, but acts responsively in a ``sensible'' manner.
keywords	Swarm Robotics; Adaptive Fabrication; Robotic Construction Communication Platform; Sensible System
series	eCAADe
email
last changed	2022/06/07 08:00

_id	ecaade2024_409
id	ecaade2024_409
authors	Zarzycki, Andrzej
year	2024
title	BIM-Driven Curriculum for Integrated Design Studios: Maintaining data interoperability and design flexibility
doi	https://doi.org/10.52842/conf.ecaade.2024.2.027
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 27–36
summary	This paper presents a curricular model for an integrated design studio focused on BIM-driven processes, satisfying the NAAB 2020's student performance criteria SC.5 and SC6. These criteria emphasize quantifiable, evidence-based design thinking by requiring the provision of "measurable environmental impacts" and "measurable outcomes of building performance." The studio, serving as a capstone project, integrates accessible design, user and regulatory requirements into building assemblies, structural and environmental systems, and life safety, underscoring the importance of measurable building performance outcomes. The adoption of computational design tools, particularly Building Information Modeling (BIM), facilitates engagement in environmental and user-focused simulations and ensures data interoperability throughout the design and post-occupancy phases. Utilizing a comprehensive set of tools, including life-cycle assessment (LCA) and energy modeling, the curriculum advances beyond simple simulations to support decision-making and multi-objective optimizations. This approach enables a new form of design thinking that incorporates a broader set of variables and considerations, encouraging students to meet various environmental impact and performance benchmarks, including LEED v.5 Certification points and Architecture 2030 energy standards. The integration of scenario simulation tools empowers students to autonomously advance their projects within a framework of constraints, marking a pedagogical shift towards faculty acting as learning facilitators and promoting student autonomy in design evaluation.
keywords	building information modeling, BIM, building performance simulations, design education
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2020_316
id	caadria2020_316
authors	Czynska, Klara
year	2020
title	Computational Methods for Examining Reciprocal Relations between the Viewshed of Planned Facilities and Historical Dominants - Their integration within the cultural landscape
doi	https://doi.org/10.52842/conf.caadria.2020.1.853
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 853-862
summary	The article presents a methodology for the assessment of the impact of new buildings on the cultural landscape, in particular the exposure of historical landmarks. While using digital analysis and a 3D city model, the methodology examines reciprocal visual relations between historical and planned buildings. The following methods have been used: a) Visual Impact Size (VIS) which enables to determine a visual impact area and the degree of architectural facility domination in space; b) comparative analysis (cumulative viewshed) which enables to determine areas where viewsheds of new investment and historical buildings overlap; c) simulation of selected views from the level of human eyesight. The proposed landscape examination methodology has been presented using the case study of Katowice, Poland. The goal was to determine reciprocal relations between historical landmarks of the Silesia Museum and tall buildings planned in the vicinity. The study used a Digital Surface Model (DSM), a 3D city model. All simulations have been performed using software developed by the author (C++).
keywords	cumulative viewshed; digital cityscape analysis; historical dominants; visual impact; VIS method
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
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. 437–446
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure’s material and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2020_071
id	caadria2020_071
authors	Carroll, Stan
year	2020
title	Managing Risk in a Research-Based Practice as Projects Scale To Construction:A Case Study
doi	https://doi.org/10.52842/conf.caadria.2020.1.065
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 65-74
summary	Research-based architectural practices often experiment along the bleeding edge of the new frontier of design and include developing methodologies unfamiliar to the construction industry. Successfully implementing the resulting research methodologies to an architectural scale requires careful consideration of risk management within a Design-Bid-Build construction project. How a firm manages the risk when scaling a research conclusion to an architectural scale is an essential aspect of assuring the success of the project. These considerations are particularly acute within firms whose research involves convoluted geometry. In the field of doubly-curved geometric material systems, the level of precision required to manage professional risk is commensurate with the level of geometric complexity. Adopting the mindset of a Medieval master mason's process within the context of twenty-first-century materials and processes can be a method toward a successful project. By performing well thought-out transfer procedures of digital data, resolving the fundamental challenges of fabrication, and including structural analysis as a part of the early design phases, experimental architectural expressions can be realized without extra financial risk to the designer.
keywords	Risk Management; Research-Based Practice; Complex Geometry; Digital Fabrication; Computational Design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	artificial_intellicence2019_147
id	artificial_intellicence2019_147
authors	Ding Wen Bao, Xin Yan, Roland Snooks, and Yi Min Xie
year	2020
title	Bioinspired Generative Architectural Design Form-Finding and Advanced Robotic Fabrication Based on Structural Performance
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_10
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2024)
summary	Due to the potential to generate forms with high efficiency and elegant geometry, topology optimization is widely used in architectural and structural designs. This paper presents a working flow of form-finding and robotic fabrication based BESO (Bi-directional Evolutionary Structure Optimization) optimization method. In case there are some other functional requirements or condition limitations, some useful modifications are also implemented in the process. With this kind of working flow, it is convenient to foreknow or control the structural optimization direction before the optimization process. Furthermore, some fabrication details of the optimized model will be discussed because there are also many notable technical points between computational optimization and robotic fabrication.
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	sigradi2020_470
id	sigradi2020_470
authors	Iasbik, Marina Pires; Martinez, Andressa Carmo Pena; Gazel, Jorge Lira de Toledo
year	2020
title	Integration of BIM and Algorithmic Design logics through data exchange between Grasshopper plugin and Revit and Archicad software
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 470-477
summary	The Algorithmic Design's integration with BIM (Building Information Modeling), allows greater potential for formal design innovation, tasks automation, greater geometry control, data assignment, and project documentation throughout its life cycle. This paper aims to assist in this integration, analyzing some plugins for conversion from Grasshopper to Archicad and Revit. Based on a parameterized social housing model, interoperability tests were carried out to compare different workflows and discuss some strategies and logics of algorithmic modeling to facilitate the communication between Grasshopper and BIM.
keywords	Algorithm design, Building information modeling, Parametric modeling, Project process, Interoperability
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	ecaade2024_4
id	ecaade2024_4
authors	Irodotou, Louiza; Gkatzogiannis, Stefanos; Phocas, Marios C.; Tryfonos, George; Christoforou, Eftychios G.
year	2024
title	Application of a Vertical Effective Crank–Slider Approach in Reconfigurable Buildings through Computer-Aided Algorithmic Modelling
doi	https://doi.org/10.52842/conf.ecaade.2024.1.421
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 421–430
summary	Elementary robotics mechanisms based on the effective crank–slider and four–bar kinematics methods have been applied in the past to develop architectural concepts of reconfigurable structures of planar rigid-bar linkages (Phocas et al., 2020; Phocas et al., 2019). The applications referred to planar structural systems interconnected in parallel to provide reconfigurable buildings with rectangular plan section. In enabling structural reconfigurability attributes within the spatial circular section buildings domain, a vertical setup of the basic crank–slider mechanism is proposed in the current paper. The kinematics mechanism is integrated on a column placed at the middle of an axisymmetric circular shaped spatial linkage structure. The definition of target case shapes of the structure is based on a series of numerical geometric analyses that consider certain architectural and construction criteria (i.e., number of structural members, length, system height, span, erectability etc.), as well as structural objectives (i.e., structural behavior improvement against predominant environmental actions) aiming to meet diverse operational requirements and lightweight construction. Computer-aided algorithmic modelling is used to analyze the system's kinematics, in order to provide a solid foundation and enable rapid adaptation for mechanisms that exhibit controlled reconfigurations. The analysis demonstrates the implementation of digital parametric design tools for the investigation of the kinematics of the system at a preliminary design stage, in avoiding thus time-demanding numerical analysis processes. The design process may further provide enhanced interdisciplinary performance-based design outcomes.
keywords	Reconfigurable Structures, Spatial Linkage Structures, Kinematics, Parametric Associative Design
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
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. 601–610
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220109
id	ijac202220109
authors	Ortner, F. Peter; Jing Zhi Tay
year	2022
title	Resilient by design: Informing pandemic-safe building redesign with computational models of resident congestion
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 129–144
summary	This paper describes a computational design-support tool created in response to safe-distancing measures enforced during the COVID-19 pandemic. The tool was developed for a specific use case: understanding congestion in crowded migrant worker dormitories that experienced high rates of COVID-19 transmission in 2020. Building from agent-based and network-based computational simulations, the tool presents a hybrid method for simulating building resident movements based on known or pre-determined schedules and likely itineraries. This hybrid method affords the design tool a novel approach to simultaneous exploration of spatial and temporal design scenarios. The paper demonstrates the use of the tool on an anonymised case study of a high-density migrant worker dormitory, comparing results from a baseline configuration against design variations that modify dormitory physical configuration and schedule. Comparisons between the design scenarios provide evidence for reflections on pandemic-resilient design and operation strategies for dor- mitories. A conclusions section considers the extent to which the model and case study results are applicable to other dense institutional buildings and describes the paper’s contributions to general understanding of configurational and operational aspects of resilience in the built environment.
keywords	Design for resilience, evidence-based design, design support, agent-based model, schedule-based model, network analysis
series	journal
last changed	2024/04/17 14:29

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 669-678
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia20_218
id	acadia20_218
authors	Rossi, Gabriella; Nicholas, Paul
year	2020
title	Encoded Images
doi	https://doi.org/10.52842/conf.acadia.2020.1.218
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 218-227.
summary	In this paper, we explore conditional generative adversarial networks (cGANs) as a new way of bridging the gap between design and analysis in contemporary architectural practice. By substituting analytical finite element analysis (FEA) modeling with cGAN predictions during the iterative design phase, we develop novel workflows that support iterative computational design and digital fabrication processes in new ways. This paper reports two case studies of increasing complexity that utilize cGANs for structural analysis. Central to both experiments is the representation of information within the data set the cGAN is trained on. We contribute a prototypical representational technique to encode multiple layers of geometric and performative description into false color images, which we then use to train a Pix2Pix neural network architecture on entirely digital generated data sets as a proxy for the performance of physically fabricated elements. The paper describes the representational workflow and reports the process and results of training and their integration into the design experiments. Last, we identify potentials and limits of this approach within the design processes.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_313
id	caadria2020_313
authors	Sanatani, Rohit Priyadarshi
year	2020
title	A Machine-Learning driven Design Assistance Framework for the Affective Analysis of Spatial Enclosures
doi	https://doi.org/10.52842/conf.caadria.2020.1.741
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 741-750
summary	There is a growing research direction that adopts an empirical approach to affective response in space, and aims at generating bodies of quantitative data regarding the correlations between spatial features and emotional states. This paper demonstrates a machine-learning driven computational framework that draws upon training data sets to predict the 'affective impact' of designed enclosures. For demonstration, it has been scripted as a Rhinoceros + Grasshopper based design tool that uses existing training data collected by the author. The data comprises of the spatial parameters of Enclosure Volume (V), Length/Width ratio (P) and Window Area/Total Internal Surface Area ratio (D) - and the corresponding emotional parameters of Valence and Arousal. The test values of these parameters are computed by defining the components of the test enclosure (walls, windows, floors and ceilings) in the script. Nonlinear regression components are run on the training datasets and the test input data is used to compute and display the real time predicted affective state on the circumplex model of affect.
keywords	Affective Analysis; Affective Computing; Design Assistance; Machine Learning; Spatial Enclosures
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2020_106
id	caadria2020_106
authors	Tian, Jieren and Yu, Chuanfei
year	2020
title	Dynamic Translation of Real-world Environment Factors and Urban Design Operation in a Game Engine - A Case Study of Central District in Tiebei New Town, Nanjing
doi	https://doi.org/10.52842/conf.caadria.2020.2.011
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 11-20
summary	The building and its urban environment are complex and dynamic data systems. Designers, who make design decisions, need the design tools to simulate the built environment, to estimate the feasibility of the design. However, the static modeling software, widely used nowadays, restricts the linkage relationship between the actual data environment and the simulation model, which lacks the dynamic constraint relationship and the construction of the loop order. Different from traditional modeling and analysis tools, simulation games, with dynamic constraint rules and real-time feedback operations, provide a new way of thinking and a perspective to observe the urban, which makes the simulation game be seen as a simplified analog system, to some extent. Therefore, this paper plan to builds a city model, based on an urban design project of an urban district of Nanjing as an example, by using the Cities: Skylines, a city simulation game with priority of traffic and zoning concept. Based on this dynamic model, the next step will evaluate the original project and carry out further optimization operations in real-time.
keywords	real-time interaction; dynamic process simulation; urban environment; city simulation system; simulated game
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaade2020_015
id	ecaade2020_015
authors	Yazici, Sevil
year	2020
title	A machine-learning model driven by geometry, material and structural performance data in architectural design process
doi	https://doi.org/10.52842/conf.ecaade.2020.1.411
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 411-418
summary	Artificial Intelligence (AI), based on interpretation of data, influences various professions including architectural design today. Although research on integrating conceptual design with Machine Learning (ML) algorithms as a subset of the AI has been investigated previously, there is not a framework towards integration of architectural geometry with material properties and structural performance data towards decision making in the early-design phase. Undertaking performance simulations require significant amount of computation power and time. The aim of this research is to integrate ML algorithms into design process to achieve time efficiency and improve design results. The proposed workflow consists of three stages, including generation of the parametric model; running structural performance simulations to collect the data, and operating the ML algorithms, including Artificial Neural Network (ANN), Non-Linear Regression (NLR) and Gaussian Mixture (GM) for undertaking different tasks. The results underlined that the system generates relatively fast solutions with accuracy. Additionally, ML algorithms can assist generative design processes.
keywords	Machine-learning; performance simulation; data-driven design; early-design phase
series	eCAADe
email
last changed	2022/06/07 07:57

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

_id	cdrf2019_265
id	cdrf2019_265
authors	Yue Qi, Ruqing Zhong, Benjamin Kaiser, Long Nguyen,Hans Jakob Wagner, Alexander Verl, and Achim Menges
year	2020
title	Working with Uncertainties: An Adaptive Fabrication Workflow for Bamboo Structures
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_25
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper presents and investigates a cyber-physical fabrication work-flow, which can respond to the deviations between built- and designed form in realtime with vision augmentation. We apply this method for large scale structures built from natural bamboo poles. Raw bamboo poles obtain evolutionarily optimized fibrous layouts ideally suitable for lightweight and sustainable building construction. Nevertheless, their intrinsically imprecise geometries pose a challenge for reliable, automated construction processes. Despite recent digital advancements, building with bamboo poles is still a labor-intensive task and restricted to building typologies where accuracy is of minor importance. The integration of structural bamboo poles with other building layers is often limited by tolerance issues at the interfaces, especially for large scale structures where deviations accumulate incrementally. To address these challenges, an adaptive fabrication process is developed, in which existing deviations can be compensated by changing the geometry of subsequent joints to iteratively correct the pose of further elements. A vision-based sensing system is employed to three-dimensionally scan the bamboo elements before and during construction. Computer vision algorithms are used to process and interpret the sensory data. The updated conditions are streamed to the computational model which computes tailor-made bending stiff joint geometries that can then be directly fabricated on-the-fly. In this paper, we contextualize our research and investigate the performance domains of the proposed workflow through initial fabrication tests. Several application scenarios are further proposed for full scale vision-augmented bamboo construction systems.
series	cdrf
email
last changed	2022/09/29 07:51

_id	caadria2020_045
id	caadria2020_045
authors	Zheng, Hao and Ren, Yue
year	2020
title	Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 707-716
summary	Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window.
keywords	Machine Learning; Artificial Intelligence; Generative Design; Geometric Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2020_347
id	caadria2020_347
authors	Budig, Michael, Heckmann, Oliver, Ng Qi Boon, Amanda, Hudert, Markus, Lork, Clement and Cheah, Lynette
year	2020
title	Data-driven Embodied Carbon Evaluation of Early Building Design Iterations
doi	https://doi.org/10.52842/conf.caadria.2020.2.303
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 303-312
summary	In the early design phases, Life Cycle Assessment can assist project stakeholders in making informed decisions on choosing structural systems and materials with an awareness of environmental sustainability through their embodied carbon content; yet embodied carbon is difficult to quantify without detailed design information in the early design stages. In response, this paper proposes a novel data-driven tool, prior to the definition of floor plan layouts to perform embodied carbon evaluation of existing building designs based on a Bayesian Neural Network (BNN) regression. The BNN is built from data drawn from existing floor plans of residential buildings, and predicts material volume and embodied carbon from generic design parameters typical in the early design stage. Users will be able to interact with the tool in Grasshopper or as an online resource, input generic design parameters, and obtain comparative visualizations based on the choice of a construction system and its environmental sustainability in a 'shoebox' interface - a simplified three-dimensional representation of a building's primary spatial units generated with the tool.
keywords	Regression; Bayesian Neural Network; High-Rise Residential Buildings
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2020_035
id	caadria2020_035
authors	Pereira, InÃªs, BelÃ©m, Catarina and LeitÃ£o, AntÃ³nio
year	2020
title	Escaping Evolution - A Study on Multi-Objective Optimization
doi	https://doi.org/10.52842/conf.caadria.2020.1.295
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 295-304
summary	The architectural field is currently experiencing a paradigm shift towards a more environmentally-aware design process. In this new paradigm, known as Performance-Based Design (PBD), building performance emerges as a guiding principle. Unfortunately, PBD entails several problems, for instance, building design is often associated with the simultaneous assessment of multiple performance criteria, which dramatically increases the complexity of the problem. In this vein, recent works claim that coupling optimization tools with PBD approaches allows for more efficient and optima-oriented strategies. This approach, known as Algorithmic Optimization, is based on the use of an optimization tool combined with a parametric model of a design to iteratively generate more efficient design alternatives. This paper focus on evaluating and comparing different classes of Multi-Objective Optimization (MOO) algorithms, namely, metaheuristics and model-based ones. In addition, in order to try to better understand the algorithms' suitability to different optimization problems, this research analyses two different MOO design problems.
keywords	Performance-Based Design; Algorithmic Optimization; Multi-Objective Optimization
series	CAADRIA
email
last changed	2022/06/07 08:00

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