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	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	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
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
doi	https://doi.org/10.52842/conf.caadria.2022.2.395
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_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
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. 727-736
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_215
id	caadria2022_215
authors	Settimi, Andrea, Vestartas, Petras, Gamerro, Julien and Weinand, Yves
year	2022
title	Cockroach: an Open-source Tool for Point Cloud Processing in CAD
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. 325-334
doi	https://doi.org/10.52842/conf.caadria.2022.2.325
summary	In the architecture, engineering and construction (AEC) sector, the use of point cloud data is not a novelty. Usually employed to retrieve data for inspecting construction sites or retrofitting pre-existing buildings, sensors like LiDAR cameras have been known to practitioners such as architects and engineers for a while now. In recent years, the growing interest in 3D data acquisition for autonomous vehicles, robotic and extended reality (XR) applications has brought to the market new compact, performant, and more accessible hardware leveraging different technologies able to provide low-cost sensing systems. Nevertheless, point clouds obtained from such sensors must be processed to extract valuable data for any design or fabrication application. Unfortunately, most advanced point cloud processing tools are written in low-level languages and are hardly accessible to the average designer or maker. Therefore, we present Cockroach: a link between computer-aided design (CAD) modeling software and low-level point cloud processing libraries. The main objective is an adaptation to C# .NET via Grasshopper visual scripting interface and C++ single-line commands in native Rhinoceros workspaces. Cockroach has proved to be a handy design tool in integrating building components with unpredictable geometries such as raw wood or mineral scraps into new design and industrial fabrication processes.
keywords	Computer-vision, Point-clouds, Data-processing, 3D modeling, CAD interface, Open-source tools, Quality education, Industry innovation and infrastructure, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_105
id	ecaade2022_105
authors	Trento, Armando, Fioravanti, Antonio, Kieferle, Joachim and Woessner, Uwe
year	2022
title	Bridging Cultural Heritage Ontologies in VR Environment - A framework for querying and reasoning on the Temple of Venus and Rome restoration and documentation
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. 177–186
doi	https://doi.org/10.52842/conf.ecaade.2022.2.177
summary	VR applied to Architectural and Archaeological Heritage has a long history: Digital models in this field are evolving from an aesthetic simulation of reality, or, rather, a representation of the visual perception, to a more complex model: an information aggregation core. The investigation presents a research panel oriented to enhance the digital survey products - point clouds, meshes, 3D models -to be used as an intelligent visual archive assigning structured knowledge contents to artefacts’ geometry. The implemented case regards the Temple of Venus and Rome. Research, in progress, has been developed by the following steps: 1) Subdividing the artefact geometry into sub- regions; 2) Developing the consolidation ontology for a few restoration classes; 3) Assigning (manually) to each artefact subcomponent, namely a mesh sub-region, a “smart label” including a link to its consolidation ontology instance. The aim is to combine the potential of VR visualization with ontology reasoning systems.
keywords	VR, Archaeological Heritage, Knowledge-based Design Systems, Restoration Ontologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_218
id	ecaade2022_218
authors	Bank, Mathias, Sandor, Viktoria, Schinegger, Kristina and Rutzinger, Stefan
year	2022
title	Learning Spatiality - A GAN method for designing architectural models through labelled sections
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. 611–619
doi	https://doi.org/10.52842/conf.ecaade.2022.2.611
summary	Digital design processes are increasingly being explored through the use of 2D generative adversarial networks (GAN), due to their capability for assembling latent spaces from existing data. These infinite spaces of synthetic data have the potential to enhance architectural design processes by mapping adjacencies across multidimensional properties, giving new impulses for design. The paper outlines a teaching method that applies 2D GANs to explore spatial characteristics with architectural students based on a training data set of 3D models of material-labelled houses. To introduce a common interface between human and neural networks, the method uses vertical slices through the models as the primary medium for communication. The approach is tested in the framework of a design course.
keywords	AI, Architectural Design, Materiality, GAN, 3D, Form Finding
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_177
id	caadria2022_177
authors	Pan, Yongjie and Zhang, Tong
year	2022
title	Outdoor Thermal Environment Assessment of Existing Residential Areas Supported by UAV Thermal Infrared and 3D Reconstruction Technology
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. 729-738
doi	https://doi.org/10.52842/conf.caadria.2022.2.729
summary	The underlying surface temperature is an effective evaluation index to study the urban micro-scale thermal environment. For surface temperature acquisition, the thermal infrared camera mounted on a unmanned aerial vehicle (UAV) can reduce field work intensity, improve data collection efficiency, and ensure high accuracy at low cost. In order to convert the 2D thermal image into a more intuitive 3D thermal model, the UAV-based thermal infrared 3D reconstruction is adopted. The key element of thermal infrared 3D model reconstruction lies in the processing of thermal infrared images with low resolution and different temperature scales. In order to improve the quality of the final thermal 3D model, this paper proposes the reconstruction of the detailed 3D mesh using visible images (higher resolution), and map then mapping thermal textures onto the mesh using thermal images (low resolution). In addition, absolute temperature values are extracted from thermal images with different temperature ranges to ensure consistence between color and temperature values in the reconstructed thermal 3D model. The thermal 3D model generated for an existing residential area in Nanjing successfully displays the temperature distribution of the underlying surface and provides a valuable basis for outdoor thermal environment assessment.
keywords	Thermal image, UAV, 3D reconstruction, Residential outdoor space, Underlying surface temperature, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.393
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	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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.525
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	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 287–295
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.2.669
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_93
id	caadria2022_93
authors	Feng, Jiajia, Liang, Yuebing, Hao, Qi, Xu, Ke and Qiu, Waishan
year	2022
title	POI Data Versus Land Use Data, Which Are Most Effective in Modelling Theft Crimes?
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. 425-434
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
summary	Alleviating crime and improving urban safety is important for sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI data. There is no agreement on which is more effective. To fill this gap, we systematically compare land use and POI data regarding their strength as well as the divergence and coherence in profiling urban functions for crime studies. Three categories of urban function features, namely the density, fraction, and diversity, are extracted from POI and land use data, respectively. Their global and local strength are compared using ordinary least square (OLS) regression and geographically weighted regression (GWR), with a case study of Beijing, China. The OLS results indicate that POI data generally outperforms land use data. The GWR models reveal that POI Density is superior to other indicators, especially in areas with concentrated commercial or public service facilities. Additionally, Land Use Fraction performs better for large-scale functional areas like green space and transportation hubs. This study provides important reference for city planners in selecting urban function indicators and modelling crimes.
keywords	POI, Land Use, Urban Functions, Theft crime, Predictive Power, SDG 16
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_154
id	ecaade2022_154
authors	Ferretti, Maddalena, Di Leo, Benedetta, Quattrini, Ramona and Vasic, Iva
year	2022
title	Creativity and Digital Transition in Central Apennine - Innovative design methods and digital technologies as interactive tools to enable heritage regeneration and community engagement
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. 187–196
doi	https://doi.org/10.52842/conf.ecaade.2022.2.187
summary	This contribution proposes strategies of reactivation of the central Apennine of Marche Region in Italy through creative design methods and virtual technologies. The research activities are connected to two related PhD projects: one focusing on architectural and urban design, the other one on heritage digitalization and new technologies and to other research activities of our interdisciplinary team. Cagli, a small town of 8.000 inhabitants, is currently undergoing socio-economic transformations that need to be addressed strategically with a cultural and spatial perspective. The research explores regenerative solutions and local development strategies to enhance the city and its cultural landscape. Participatory processes aided by digital tools and innovative design methods are tested in Cagli’s living lab. The final output of the overall research is a “Reactive Map” combining a trans-scalar and multidisciplinary territorial analysis with visions to identify “potential spaces”. The map is a design tool to define a shared strategy of enhancement of the city and its heritage. With this paper we present one of the methodological steps of the research, a WEB-APP built upon a point clouds database and assessed through a preliminary user test. The highly descriptive 3D environment is able to collect analysis and to be enriched in a participatory way during planned activities of co-thinking. The 3D environment, improved with interviews, plans, historical pictures and other media contents, is also paired with a virtual tour to offer a different representation of the “potential spaces”. The fully boosting 3D digital technology thus represents a viable and effective solution to involve citizens and an innovative and interdisciplinary tool for knowledge advancement in the fields of architectural and urban design and heritage regeneration.
keywords	Tangible and Intangible Heritage, Co-Thinking, Trans-Scalar Approach, Narrative, Point Clouds Exploitation, Interactive Annotation, Virtual Reality
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_458
id	caadria2022_458
authors	Gong, Pixin, Huang, Xiaoran, Huang, Chenyu and White, Marcus
year	2022
title	Machine Learning-Based Walkability Modeling in Urban Life Circle
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. 645-654
doi	https://doi.org/10.52842/conf.caadria.2022.1.645
summary	With China's fast urbanization, the study of the walkability of residents' life circles has become critical to improve people's quality of life. Traditional walkability calculations are based on Lawrence Frank's theory. However, the weighted calculation method cannot be adapted to ever-changing and complicated scenarios as the scope and topic of research transforming. This study investigated walkability at the community level by combining machine learning techniques with multi-source data. Feature indicators affecting walkability were estimated from multi-source data. Machine learning was used to refine the weighting calculation under the previous indicator framework. We compared the performance of 20 regression models from 6 different machine learning algorithms for estimating the walkability of 14578 communities in downtown Shanghai. It is concluded that the Bagged Tree Model (R2=0.86, RMSE=0.36862) achieves the best performance, which is used to revise the initial walkability index values. The workflow proposed in this paper allows for rapid application of expert empirical consensus to comprehensive urban design and detailed urban governance in the future.
keywords	Life Circle, Walkability Indicator, Multi-source Data, Machine Learning, Refined Urban Design, SDG 3, SDG 10, 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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.111
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	ecaade2022_223
id	ecaade2022_223
authors	Tuzun Canadinc, Seda and Yan, Wei
year	2022
title	3D-Model-Based Augmented Reality for Enhancing Physical Architectural Models
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. 495–504
doi	https://doi.org/10.52842/conf.ecaade.2022.2.495
summary	In the presentation of architectural projects, physical models are still commonly used as a powerful and effective representation for building design and construction. On the other hand, Augmented Reality (AR) promises a wide range of possibilities in visualizing and interacting with 3D physical models, enhancing the modeling process. To benefit both, we present a novel medium for architectural representation: a marker-less AR powered physical architectural model that employs dynamic digital features. With AR enhancement, physical capabilities of a model could be extended without sacrificing its tangibility. We developed a framework to investigate the potential uses of 3D-model- based AR registration method and its augmentation on physical architectural models. To explore and demonstrate integration of physical and virtual models in AR, we designed this framework providing physical and virtual model interaction: a user can manipulate the physical model parts or control the visibility and dynamics of the virtual parts in AR. The framework consists of a LEGO model and an AR application on a hand-held device which was developed for this framework. The AR application utilizes a marker-less AR registration method and employs a 3D-model-based AR registration. A LEGO model was proposed as the physical 3D model in this registration process and machine learning training using Vuforia was utilized for the AR application to recognize the LEGO model from any point of view to register the virtual models in AR. The AR application also employs a user interface that allows user interaction with the virtual parts augmented on the physical ones. The working application was tested over its registration, physical and virtual interactions. Overall, the adoption of AR and its combination with physical models, and 3D-model-based AR registration allow for many advantages, which are discussed in the paper.
keywords	Augmented Reality, AR, 3D-model based AR, Architectural Representation, Architectural Modeling
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_371
id	cdrf2022_371
authors	Viktória Sándor, Mathias Bank, Kristina Schinegger, and Stefan Rutzinger
year	2022
title	Collapsing Complexities: Encoding Multidimensional Architecture Models into Images
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_32
summary	The paper details a 3D to 2D encoding method, which can store complex digital 3D models of architecture within a single image. The proposed encoding works in combination with a point cloud notation and a sequential slicing operation where each slice of points is stored as a single row of pixels in the UV space of a 1024?×?1024 image. The performance of the notation system is compared between a StyleGan2 and existing image editing methods and evaluated through the production of new 3D models of houses with material attributes. The uncovered findings maintain the relatively high level of detail stored through the encoding while allowing for innovative ways of form-finding—producing new and unseen 3d models of architectural houses.
series	cdrf
email
last changed	2024/05/29 14:03

_id	ecaade2022_168
id	ecaade2022_168
authors	Abdulmawla, Abdulmalik, Schneider, Sven, Koenig, Reinhard, Bielik, Martin and Fuchkina, Ekaterina
year	2022
title	Parametric Urban Data Structuring and Spatial Query - Advanced data mapping and selection methods for parametric modelling environments
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. 277–286
doi	https://doi.org/10.52842/conf.ecaade.2022.2.277
summary	This paper presents a method for organising urban data inside the CAD environment into a hierarchical structure, which promotes the ease of transferring information between all available urban elements, from streets to buildings passing by the plots and blocks. This is done using parametric methods that map the urban data using the available CAD and GIS records. Finally, the paper presents a couple of example scenarios where such methods are most needed and how much they could facilitate more detailed and complex data to be accessed, compared, and analysed.
keywords	Urban Query, Urban Geometry, Spatial Mapping
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_278
id	ecaade2022_278
authors	Gopalakrishnan, Srilalitha, Srikanth, Anjanaa, Hablani, Chirag and Schroepfer, Thomas
year	2022
title	Measuring Impacts of Vertically Integrated Pedestrian Network Configurations on Urban Space Use in Dense Built Environments
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. 307–316
doi	https://doi.org/10.52842/conf.ecaade.2022.2.307
summary	Integrated mixed-use developments are increasingly taking the form of vertical extensions of urban spaces on the ground. The spatial networks within the evolving vertical neighbourhoods, their relationships with the larger urban fabric, and the user interactions within these complex multi-layered urban built environments are numerous and varied. This paper presents an analytical framework to map and analyse the pedestrian connectivity within the vertically integrated urban open space network and its interactions with the ground level urban fabric using a Network Science-based approach. The research uses Kampung Admiralty, a first-of-its-kind building site scale 'vertical city' prototype in Singapore, as a case study. A 3d pedestrian network link model mapping the pedestrian connectivity within the development is generated and analysed to understand the flows and accessibility to the vertically distributed urban open spaces. This 3d pedestrian link model is further combined with the 2d urban walking network at the ground level to generate an integrated neighbourhood-level walkability analysis. Analysing the two-dimensional connectivity at the ground level and comparing the influence of linking the three-dimensional vertical connectivity to the ground network generates valuable design insights into the spatial performance of vertically integrated developments in their immediate urban context.
keywords	Network Science, sDNA, Urban Pedestrian Network, Vertical Urban Environments, Vertical Connectivity
series	eCAADe
email
last changed	2024/04/22 07:10

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
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

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