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	ascaad2022_110
id	ascaad2022_110
authors	Salem, Mona; Moussa, Ramy
year	2022
title	A Hybrid Approach Based on Building Physics and Machine Learning for Thermal Comfort Prediction in Smart Buildings
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 253-263
summary	One of the most important challenges facing the world is the application of modern technology in order to create smart buildings that achieve sustainable development goals (SDGs). Thermal comfort and reduction of energy consumption in buildings are considered important factors which, in turn, are reflected in creating a healthy environment and improving human productivity. Internet of Things (IoT) provides an ideal solution for collecting real-time data on the factors affecting indoor thermal comfort and energy consumption. However, comfort level is subjective and depends on many factors, which may not be learned by conventional models, an integrated model depending on thermal comfort factors is needed. In this work, a hybrid physics-based model incorporated with machine learning techniques is used for the prediction of thermal comfort inside buildings. XGBoost (eXtreme Gradient Boost) algorithm method was used due to its abilities to handle complex problems. A calculated dataset was extracted from the physics-based model gathered with the environmental variables data such as humidity, moisture, temperature, and air velocity collected from IoT devices. The results show an improvement in the prediction of the thermal comfort approach as compared with the conventional models. The XGBoost algorithm can exhibit an effective solution for eliminating deficiencies of traditional models and can be used when designing smart buildings, simulating, and evaluating the designed buildings, controlling energy consumption, and achieving thermal comfort.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	ecaade2024_101
id	ecaade2024_101
authors	Yu, Jiaqi; Guo, Kening; Bai, Zishen; Wen, Zitong
year	2024
title	Application of Artificial Neural Network for Predicting U-Values of Building Envelopes in Temperate Zones
doi	https://doi.org/10.52842/conf.ecaade.2024.1.585
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. 585–592
summary	Due to the global energy deficit, building energy consumption has become a significant issue in recent years. Many researchers have focused on building energy consumption simulations to manage energy consumption accurately and provide a comfortable indoor environment for occupants. In building energy simulations, accurate input of building parameters is essential. As important thermal parameters, the thermal transmittance (U-value) of building envelopes can affect building operational energy consumption. In most building energy simulation studies, the U-value was set to the theoretical U-value which was a fixed value. However, the U-value constantly varies due to several environmental impacts, especially fluctuating air temperature and relative humidity (T/RH). Thus, the U-values are dynamic in actual situations, and inputting dynamic U-values into building energy simulations can reduce the gap between the simulation and the actual situation. In this study, the dynamic U-values of conventional cavity envelopes in temperate zones were predicted by an artificial neural network (ANN) model. Firstly, the in-situ dynamic U-value measurement was conducted in Sheffield, the UK, from summer to winter in 2022. The heat flow meter method was applied, and the tested envelope was a conventional cavity envelope widely used in the UK. The indoor and outdoor T/RH were measured and recorded as well. Then, the measured data were applied to train the optimal ANN model. The input parameters included the indoor and outdoor T/RH, and the output parameter was the dynamic U-value. Finally, the prediction results obtained by the optimal ANN model were closely correlated with the measured dynamic U-value. This quantitative study of dynamic U-values examined the relationship between dynamic U-values of conventional cavity envelopes and environmental factors, which can provide reliable information for improving the inputting patterns of building parameters and the accuracy of the building energy simulation.
keywords	Artificial Neural Network Model, In-situ U-value Measurement, Dynamic U-value Prediction, Conventional Cavity Envelopes
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2022_522
id	caadria2022_522
authors	Cheng, Sifan, Leung, Carson Ka Shut and van Ameijde, Jeroen
year	2022
title	Evaluating the Accessibility of Amenities toward Walkable Neighourhoods: an Integrated Method for Testing Alternatives in a Generative Urban Design Process
doi	https://doi.org/10.52842/conf.caadria.2022.1.495
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 495-504
summary	Studies have shown that walkable communities reduce traffic-related pollution and the risk of chronic illnesses, promote economic growth and prosperity, and stimulate community participation and the growth of social capital. To assess the walkability of urban areas, various methodologies have been developed around shortest-distance calculations between various points of interest (POIs), yet their outcomes do not guide potential urban design improvements. The absence of appropriate measurements and procedures that may give quantitative and actionable feedback to support design decision-making is one of the primary issues in building walkable neighborhoods. The work presented in this paper revolves around a new workflow, that employed Urbano, a mobility simulation and assessment tool, and integrated it within a generative design process to allowing for the quantitative evaluation on amenity accessibility for several alternative design scenarios for a case study site in Mong Kok, Hong Kong. The results show how this data-driven urban design process benefits from generative techniques to produce solutions with improved contextual connectivity, energy-efficient urban form, and good quality public spaces that contribute to the walkability of neighbourhoods.
keywords	Generative Urban Design, Walkability, Urbano, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 313-322
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_352
id	caadria2022_352
authors	Duran, Ayca, Iseri, Orcun Koral, Meral Akgul, Cagla, Kalkan, Sinan and Gursel Dino, Ipek
year	2022
title	Compiling Open Datasets to Improve Urban Building Energy Models with Occupancy and Layout Data
doi	https://doi.org/10.52842/conf.caadria.2022.2.669
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 669-678
summary	Urban building energy modelling (UBEM) has great potential for assessing the energy performance of the existing building stock and exploring various actions targeting energy efficiency. However, the precision and completeness of UBEM models can be challenged due to the lack of available and reliable datasets related to building occupant and layout information. This study presents an approach that aims to augment UBEM with open-data sources. Data collected from open data sources are integrated into UBEM in three steps. Step (1) involves the generation of occupant profiles from census data collected from governmental institutions. Step (2) relates to the automated generation of building plan layouts by extracting data on building area and number of rooms from an online real-estate website. Results of Steps (1) and (2) are incorporated into Step (3) to generate residential units with layouts and corresponding occupant profiles. Finally, we make a comparative analysis between data-augmented and standard UBEM based on building energy use and occupant thermal comfort. The initial results point to the importance of detailed, precise energy models for reliable performance analysis of buildings at the urban scale. 0864108000
keywords	urban building energy modelling, occupancy, residential building stock, unit layout Information, open-source datasets, energy demand, indoor thermal comfort, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_196
id	caadria2022_196
authors	Grisiute, Ayda, Shi, Zhongming, Chadzynski, Arkadiusz, Silvennoinen, Heidi, von Richthofen, Aurel and Herthogs, Pieter
year	2022
title	Automated Semantic SWOT Analysis for City Planning Targets: Data-driven Solar Energy Potential Evaluations for Building Plots in Singapore
doi	https://doi.org/10.52842/conf.caadria.2022.1.555
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 555-564
summary	Singapore‚s urban planning and management is cross-domain in nature and need to be assessed using multi-domain indicators ‚ such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation.
keywords	Semantic Web, Knowledge Graphs, SWOT analysis, energy-driven urban design, SDG 11, SDG 7
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_239
id	caadria2022_239
authors	Huang, Chenyu, Zhang, Gengjia, Yin, Minggang and Yao, Jiawei
year	2022
title	Energy-driven Intelligent Generative Urban Design, Based on Deep Reinforcement Learning Method With a Nested Deep Q-R Network
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 233-242
summary	To attain "carbon neutrality," lowering urban energy use and increasing the use of renewable resources have become critical concerns for urban planning and architectural design. Traditional energy consumption evaluation tools have a high operational threshold, requiring specific parameter settings and cross-disciplinary knowledge of building physics. As a result, it is difficult for architects to manage energy issues through 'trial and error' in the design process. The purpose of this study is to develop an automated workflow capable of providing urban configurations that minimizing the energy use while maximizing rooftop photovoltaic power potential. Based on shape grammar, parametric meta models of three different urban forms were developed and batch simulated for its energy performance. Deep reinforcement learning (DRL) is introduced to find the optimal solution of the urban geometry. A neural network was created to fit a real-time mapping of urban form indicators to energy performance and was utilized to predict reward for the DRL process, namely a Deep R-Network, while nested within a Deep Q-Network. The workflow proposed in this paper promotes efficiency in optimizing the energy performance of solutions in the early stages of design, as well as facilitating a collaborative design process with human-machine interaction.
keywords	energy-driven urban design, intelligent generative design, rooftop photovoltaic power, deep reinforcement learning, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_5
id	architectural_intelligence2022_5
authors	Jiading Zhong, Jianlin Liu, Yongling Zhao, Jianlei Niu & Jan Carmeliet
year	2022
title	Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00008-7
source	Architectural Intelligence Journal
summary	Pressing problems in urban ventilation and thermal comfort affecting pedestrians related to current urban development and densification are increasingly dealt with from the perspective of climate change adaptation strategies. In recent research efforts, the prime objective is to accurately assess pedestrian-level wind (PLW) environments by using different simulation approaches that have reasonable computational time. This review aims to provide insights into the most recent PLW studies that use both established and data-driven simulation approaches during the last 5 years, covering 215 articles using computational fluid dynamics (CFD) and typical data-driven models. We observe that steady-state Reynolds-averaged Navier-Stokes (SRANS) simulations are still the most dominantly used approach. Due to the model uncertainty embedded in the SRANS approach, a sensitivity test is recommended as a remedial measure for using SRANS. Another noted thriving trend is conducting unsteady-state simulations using high-efficiency methods. Specifically, both the massively parallelized large-eddy simulation (LES) and hybrid LES-RANS offer high computational efficiency and accuracy. While data-driven models are in general believed to be more computationally efficient in predicting PLW dynamics, they in fact still call for substantial computational resources and efforts if the time for development, training and validation of a data-driven model is taken into account. The synthesized understanding of these modeling approaches is expected to facilitate the choosing of proper simulation approaches for PLW environment studies, to ultimately serving urban planning and building designs with respect to pedestrian comfort and urban ventilation assessment.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	caadria2022_277
id	caadria2022_277
authors	Akbar, Zuardin, Wood, Dylan, Kiesewetter, Laura, Menges, Achim and Wortmann, Thomas
year	2022
title	A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.393
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 393-402
summary	This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R;0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community.
keywords	data-driven model, machine learning, material programming, smart material, timber structure, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_086
id	ascaad2022_086
authors	Chehab, Aya; Nakhal, Bilal
year	2022
title	Exploring Virtual Reality as an Approach to Resurrect Destroyed Historical Buildings: An Approach to Revive the Destroyed "Egg Building" through VR
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 609-631
summary	An important part of a city, that gives it a sense of community and character, is its history. One way of acknowledging this heritage is by preserving historic building and structures. Old buildings are witnesses to the aesthetic and cultural history of a city, helping to give people a sense of place and connection to the past. Unfortunately, despite their importance within the city, historical buildings are most of the time subject to demolition and to be replaced- leaving behind stories told and untold of what use to be. The paper, therefore, aims to explore the capability of the metaverse, using virtual reality touring, to revive the memory of historical buildings that are subject to fade. Where preserving historical buildings can not only act as a symbol of grandeur but is also vital for reviving the community’s collective memory. The case study focused upon in the research paper shows a first step in the development of an immersive virtual tour for the significant building of “The Egg” or “Beirut City Center” in Downtown- which is a building that witnessed a series of unfortunate events that lead to destruction, erasure, and demolition of the building. Therefore, examining the recovery and revival of this unique historic site in an unconventional way which is in the metaverse, specifically the Virtual Reality (VR). The paper assumes that virtual reality, as the main metaverse approach, would help people ‘remember’ and ‘mentally revive’ the destroyed historical buildings that once acted as the building blocks in the impacted city. To prove this hypothesis, two different methodologies will be used, by theorical analysis and literature review, such as analyzing the main keyword, and analyzing datum from previous works. The second method will rely on the physical methodology, where virtual 3D Models will be built in a computer software, Autodesk Revit, then imported within a VR experience for an enhanced experience within the historical site to preserve the historic buildings and revive the collective memory within the community, enabling people to view how these historic sites once were and how they have now become.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ijac202220216
id	ijac202220216
authors	Keyvanfar, Ali; Arezou Shafaghat; Muhamad SF Rosley
year	2022
title	Performance comparison analysis of 3D reconstruction modeling software in construction site visualization and mapping
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 453–475
summary	Unmanned aerial vehicle (UAV) technology has overcome the limitations of conventional construction management methods using advanced and automated visualization and 3D reconstruction modeling techniques. Although the mapping techniques and reconstruction modeling software can generate real-time and high-resolution descriptive textural, physical, and spatial data, they may fail to develop an accurate and complete 3D model of the construction site. To generate a quality 3D reconstruction model, the construction manager must optimize the trade-offs among three major software-selection factors: functionalities, technical capabilities, and the system hardware specifications. These factors directly affect the robust 3D reconstruction model of the construction site and objects. Accordingly, the purpose of this research was to apply nine well-established 3D reconstruction modeling software tools (DroneDeploy, COLMAP, 3DF+Zephyr, Autodesk Recap, LiMapper, PhotoModeler, 3D Survey, AgiSoft Photoscan, and Pix4D Mapper) and compare their performances and reliabilities in generating complete 3D models. The research was conducted in an eco-home building at the University of Technology, Malaysia. A series of regression analyses were conducted to compare the performances of the selected 3D reconstruction modeling software in alignment and registration, distance computing, geometric measurement, and plugin execution. Regression analysis determined that among the software programs, LiMapper had the strongest positive linear correlation with the ground truth model. Furthermore, the correlation analysis showed a statistically significant p-value for all software, except for 3D Survey. In addition, the research found that Autodesk Recap generated the most-robust and highest-quality dense point clouds. DroneDeploy can create an accurate point cloud and triangulation without using many points as required by COLMAP and LiMapper. It was concluded that most of the software is robustly, positively, and linearly correlated with the corresponding ground truth model. In the future, other factors involving software selection should be studied, such as vendor-related, user-related, and automation factors.
keywords	Construction site visualization, unmanned aerial vehicle, photogrammetry, 3D reconstruction modeling, multi-view-stereopsis, structure-from-motion, ANOVA and regression analysis
series	journal
last changed	2024/04/17 14:29

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

_id	caadria2022_464
id	caadria2022_464
authors	Liu, Xinyu and van Ameijde, Jeroen
year	2022
title	Data-driven Research on Street Environmental Qualities and Vitality Using GIS Mapping and Machine Learning, a Case Study of Ma On Shan, Hong Kong
doi	https://doi.org/10.52842/conf.caadria.2022.1.485
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 485-494
summary	In a post-carbon framework, data-driven methods can be used to assess the environmental quality and sustainability of urban streetscape. Streets are an important part of people's daily lives and provide places for social interaction. Therefore, in this study, the relationship between street quality and street vibrancy is measured using the new town of Ma On Shan, Hong Kong as a study area. Firstly, machine learning was used to identify the physical features of streets through geographic information collection and streetscape image acquisition. Secondly, previous measurement algorithms are combined to calculate the greenness, walkability, safety, imageability, enclosure, and complexity of streets. Thirdly, secondary calculations and visualisations were carried out on a Geographic Information System (GIS) platform to observe the current distribution of street qualities. Finally, the relationship between street quality and vibrancy was analysed using SPSS statistical analysis software. The results show that walkability has a positive effect on street vitality, whereas safety and complexity have a negative effect on street vitality. This study demonstrates how the quantitative assessment of urban street environments can be used as a reference for building a green, low-carbon, healthy, and walkable city.
keywords	Street Quality, Geographic Information Systems, Machine Learning, Image Segmentation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_100
id	caadria2022_100
authors	Oghazian, Farzaneh, Brown, Nathan and Davis, Felecia
year	2022
title	Calibrating a Formfinding Algorithm for Simulation of Tensioned Knitted Textile Architectural Models
doi	https://doi.org/10.52842/conf.caadria.2022.1.111
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 111-120
summary	This paper presents an optimization-based calibration process for tuning a digital formfinding algorithm used with knitted textile materials in architectural tension structures. 3D scanning and computational optimization are employed to accurately approximate a physical model in a digital workflow that can be used to establish model settings for future exploration within a knit geometric typology. Several aspects of the process are investigated, including different optimization algorithms and various approaches to data extraction. The goal is to determine the appropriate optimization method and data extraction, as well as automate the process of adjusting formfinding settings related to the length of the meshes associated with the knitted textile behavior. The calibration process comprises three steps: extract data from a 3D scanned model; determine the bounds of formfinding settings; and define optimization variables, constraints, and objectives to run the optimization process. Knitted textiles made of natural yarns are organic materials and when used at the industrial level can satisfy DSG 9 factors to promote sustainable industrialization and foster innovation in building construction through developing sustainable architectural systems. The main contributions of this paper are calibrated digital models of knitted materials and a comparison of the most effective algorithms and model settings, which are a starting point to apply this process to a wider range of knit geometries. These models enhance the implementation and further development of novel architectural knitted systems.
keywords	Tensioned Knitted Textiles, Computational Design, Formfinding, Calibrating, Optimization, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_405
id	caadria2022_405
authors	Onishi, Ryo, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2022
title	A Remote Sharing Method of 3D Physical Objects Using Instance-Segmented Real-Time 3D Point Cloud for Design Meeting
doi	https://doi.org/10.52842/conf.caadria.2022.2.395
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 395-404
summary	In the field of architecture and urban design, physical models are used in design meetings. Furthermore, teleconferencing via the internet has begun to be widely used in society due to COVID-19 and in preparation for disasters. Although conventional web conferencing can share only 2D information through screens, it is expected that interactive screen sharing of physical objects will enable smoother remote conferencing. A system that can manipulate point clouds in clusters by dividing real-time point clouds captured from 3D real objects by distance has been reported as a way to share physical objects. However, because the point clouds are divided by distance between the two clusters when the point clouds get closer than some threshold, they become treated as a single object. In this study, we aim to develop a system that uses instance segmentation to divide point clouds by region rather than by distance between objects. This system is expected to contribute to the realisation of better architectural and urban design processes without any misunderstandings among the parties involved and to the reduction of unnecessary energy consumption due to travel for face-to-face meetings.
keywords	remote meeting, fast point cloud, instance segmentation, three-dimensional remote sharing, mixed reality, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_184
id	caadria2022_184
authors	Sateei, Shahin, Roup, Mattias and Johansson, Mikael
year	2022
title	Collaborative Design Review Sessions in Virtual Reality: Multi-Scale and Multi-User
doi	https://doi.org/10.52842/conf.caadria.2022.1.029
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 29-38
summary	The use of Virtual Reality (VR) for design reviews in projects is becoming more common in construction. However, the use of VR in these processes has been limited to been used more as a complementary reviewing tool alongside information medias such as 2D drawings and 3D models. Furthermore, immersive VR has been argued to have limitations when it comes to orientation and understanding and reasoning about functional links between physical layouts in a facility. This paper presents a case study of a VR system used during design reviews that support end-users to switch between different representations and scale i.e., miniature model/bird-eye view, and a 1:1 scale experience of the facility. The data gathered, consisted of recorded observation of the VR based design review process and study what type of discussion and design errors that was found during two VR-workshops connected to a new elementary school. The result shows, that by supporting switching between miniature model and 1:1 scale VR experience facilitated spatial orientation and understanding and collaboration across disciplines in the project. The study also show how collaborative immersive VR can be used as an efficient communication-tool during the design process in a real-world project.
keywords	Virtual Reality, VR, Immersive virtual environments, Collaboration, Levels of detail, SDG 4, SDG 9, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2023_317
id	ecaade2023_317
authors	Zamani, Alireza, Mohseni, Alale and Bertug Çapunaman, Özgüç
year	2023
title	Reconfigurable Formwork System for Vision-Informed Conformal Robotic 3D Printing
doi	https://doi.org/10.52842/conf.ecaade.2023.1.387
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 1, Graz, 20-22 September 2023, pp. 387–396
summary	Robotic additive manufacturing has garnered significant research and development interest due to its transformative potential in architecture, engineering, and construction as a cost-effective, material-efficient, and energy-saving fabrication method. However, despite its potential, conventional approaches heavily depend on meticulously optimized work environments, as robotic arms possess limited information regarding their immediate surroundings (Bechthold, 2010; Bechthold & King, 2013). Furthermore, such approaches are often restricted to planar build surfaces and slicing algorithms due to computational and physical practicality, which consequently limits the feasibility of robotic solutions in scenarios involving complex geometries and materials. Building on previous work (Çapunaman et al., 2022), this research investigates conformal 3D printing of clay using a 6 degrees-of-freedom robot arm and a vision-based sensing framework on parametrically reconfigurable tensile hyperbolic paraboloid (hypar) formwork. In this paper, we present the implementation details of the formwork system, share findings from preliminary testing of the proposed workflow, and demonstrate application feasibility through a design exercise that aims to fabricate unique components for a poly-hypar surface structure. The formwork system also offers parametric control over generating complex, non-planar tensile surfaces to be printed on. Within the scope of this workflow, the vision-based sensing framework is employed to generate a digital twin informing iterative tuning of the formwork geometry and conformal toolpath planning on scanned geometries. Additionally, we utilized the augmented fabrication framework to observe and analyze deformations in the printed clay body that occurs during air drying. The proposed workflow, in conjunction with the vision-based sensing framework and the reconfigurable formwork, aims to minimize time and material waste in custom formwork fabrication and printing support materials for complex geometric panels and shell structures.
keywords	Robotic Fabrication, Conformal 3D Printing, Additive Manufacturing, Computer-Vision, Reconfigurable Formwork
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2022_33
id	caadria2022_33
authors	Alva, Pradeep, Mosteiro-Romero, Martin, Miller, Clayton and Stouffs, Rudi
year	2022
title	Digital Twin-Based Resilience Evaluation of District-Scale Archetypes
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 525-534
summary	District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application for a 170ha district-scale university campus as a pilot. The impact on the built environment is simulated with pandemic (COVID-19) and climate change scenarios. The former can be observed through varying occupancy rates and average cooling loads in the buildings during the lockdown period. The digital twin dashboard was built with visualisations of the 3D campus, real-time data from sensors, energy demand simulation results from the City Energy Analyst (CEA) tool, and occupancy rates from WiFi data. The ongoing work focuses on formulating a resilience assessment metric to measure the robustness of buildings to these disruptions. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show that the digital twin approach can support decarbonising initiatives for cities.
keywords	Digital twin, City Information Modelling, Planning Support System, energy demand model, SGD 11, SGD 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_336
id	caadria2022_336
authors	Araujo, Goncalo, Santos, Luis, Leitao, Antonioand Gomes, Ricardo
year	2022
title	AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas
doi	https://doi.org/10.52842/conf.caadria.2022.2.689
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 689-698
summary	Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.
keywords	urban building energy modelling, algorithmic design, machine learning in Architecture, optimization of urban areas, SDG 7, SDG 12, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2023_219
id	sigradi2023_219
authors	Fiuza, Rebeca, Cardoso, Daniel, Moreira, Eugenio, Colares, Teresa, Freitas, Vitória and Paiva, Ricardo
year	2023
title	Correlations between urban and demographic data and COVID-19 data: a case study in Fortaleza, Brazil
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1667–1678
summary	COVID-19 was a sanitary crisis of international impact. However, its effects weren’t experienced equally. In Fortaleza, epidemiological reports (2021;2022) point to different infection patterns between high Human Development Index (HDI) and low HDI neighborhoods, which surfaced the hypothesis that certain territories’ characteristics could correlate to COVID-19 data. This article describes a phase of a three-phase research, whose objective is to identify correlations between urban and demographic (UD) data to COVID-19 data. To this, a literature review was done to select seven UD variables and four COVID-19 ones, then, Spearman’s correlation was applied in four pandemic time frames (TF). Results show that literacy rates, monthly income and energy have either low or moderate positive correlations with contamination rates in most TF. However, they’ve shown low or moderate correlations with lethality rates in three TF. Population density showed low positive correlations to either lethality rates or total number of deaths in three TF.
keywords	COVID-19, Urban Data, Demographic Data, Spearman's Coefficient Correlation, Public Health
series	SIGraDi
email
last changed	2024/03/08 14:09

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