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	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	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	ecaade2022_203
id	ecaade2022_203
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Perspectival GAN - Architectural form-making through dimensional transformation
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 1, Ghent, 13-16 September 2022, pp. 341–350
doi	https://doi.org/10.52842/conf.ecaade.2022.1.341
summary	With the ascendance of Generative Adversarial Networks (GAN), promising prospects have arisen from the abilities of machines to learn and recognize patterns in 2D datasets and generate new results as an inspirational tool in architectural design. Insofar as the majority of ML experiments in architecture are conducted with imagery based on readily available 2D data, architects and designers are faced with the challenge of transforming machine-generated images into 3D. On the other hand, GAN-generated images are found to be able to learn the 3D information out of 2D perspectival images. To facilitate such transformation from 2D and 3D data in the framework of deep learning in architecture, this paper explores making new architectural forms from flat GAN images by employing traditional tools of projective geometry. The experiments draw on Brook Taylor’s 19th- century theorem of inverse projection system for creating architectural form from perspectival information learned from GAN images of Swiss alpine architecture. The research develops a parametric tool that automates the dimensional transformation of 2D images into 3D architectural forms. This research identifies potential synergic interactions between traditional tools and techniques of architects and deep learning algorithms to achieve collective intelligence in designing and representing creative architecture forms between humans and machines.
keywords	Machine Learning, GAN, Architectural Form, Perspective Projection, Inverse Perspective, Digital Representation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_217
id	ecaade2022_217
authors	Panagiotidou, Vasiliki and Koerner, Andreas
year	2022
title	From Intricate to Coarse and Back - A voxel-based workflow to approximate high-res geometries for digital environmental simulations
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 1, Ghent, 13-16 September 2022, pp. 491–500
doi	https://doi.org/10.52842/conf.ecaade.2022.1.491
summary	Digital environmental simulations can present a computational bottleneck concerning the complexity of geometry. Therefore, a series of workarounds, ranging from cloud-based solutions to machine learning simulations as surrogate simulations are conventionally applied in practice. Concurrently, contemporary advances in procedural modelling in architecture result in design concepts with high polygon counts. This leads to an ever- increasing resolution discrepancy between design and analysis models. Responding to this problem, this research presents a step-by-step approximation workflow for handling and transferring high-resolution geometries between procedural modelling and environmental simulation software. The workflow is intended to allow designers to quickly assess a design’s interaction with environmental parameters such as airflow and solar radiation and further articulate them. A controllable voxelization procedure is applied to approximate the original geometry and therefore reduce the resolution. Controllable in this context refers to the user’s ability to locally adjust the voxel resolution to fit design needs. After export and simulation, 3d results are imported back into the design environment. The colour properties are re-mapped onto the original high- resolution geometry following a weighted proximity technique. The developed data transfer pipeline allows designers to integrate environmental analysis during initial design steps, which is essential for accessibility in the design profession. This can help to environmentally inform generative designs as well as to make simulation workflows more accessible when working with a wider range of geometries. In this, it reduces the perceived discrepancy between the concept and simulation model. This eases the use and allows a wider audience of users to develop co-creation processes between computation, architecture, and environment.
keywords	Simulation, Accessibility, Computation, Environmental Data, Workflow
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ascaad2022_065
id	ascaad2022_065
authors	David, Joao; Leitao, Antonio
year	2022
title	Getting a Handle on Floor Plan Analysis: Door Classification in Floor Plans and a Survey on Existing Datasets
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. 221-236
summary	Floor plan interpretation and reconstruction is crucial to enable the transformation of drawings to 3D models or different digital formats. It has recently taken advantage of neural-based architectures, especially in the semantic segmentation field. These techniques perform better than traditional methods, but the results depend mainly on the data used to train the networks, which is often crafted for the specific task being performed, making it hard to reuse for different purposes. In this paper, we conduct a literature survey on the existing datasets for floor plan analysis, and we explore how information regarding door placement and orientation can be recovered without having to change the initial data or model. We propose a two-step recognition method based on image segmentation followed by classification of cropped zones to allow data augmentation during training. In the process, we generate a dataset consisting of 35000 annotated door images extracted from an existing dataset.
series	ASCAAD
email
last changed	2024/02/16 13:29

_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	ecaade2022_396
id	ecaade2022_396
authors	Hamzaoglu, Begüm, Özkar, Mine and Aydin, Serdar
year	2022
title	Towards a Digital Practice of Historical Stone Carvings
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. 227–234
doi	https://doi.org/10.52842/conf.ecaade.2022.2.227
summary	Local traditional crafts in various parts of the world are being transformed by digitalization in tandem with broader social and economic changes. Mardin, a historical and cultural hub in southeast Anatolia, presents an exemplary case with its stone architecture. Whereas the number of skilled craftsmen is diminishing, digital fabrication ateliers are increasingly in demand in the city and rising in number. Training programs have already started integrating CNC milling-based techniques. However, despite the growing interest in adapting computational processes, how the craft knowledge is documented and conveyed to multiple actors for maintaining and even increasing the quality of workmanship is yet to be explored. We present a novel way to document carving procedures and to create an inventory of the 3D motifs using cross-sections as complements to front views. The research engages end-user participants of different backgrounds, such as stone cutting technologies and architecture, with little or no practical knowledge of digital manufacturing. The work focuses on a selection of motifs from the Syriac stone carving heritage in Mardin, the documentation of which is very limited. The proposed workflow begins with recording the surface depth and the variations in the cross-section using digital scans. In the second stage, we consider the potential subtractive transformations that result in the final form and reconstruct them as milling operations with a parametric and procedural modeling approach. Various milling processes are derived by relating the shapes to the available cutting tools and materials. The study contributes to creating the inventory of an engraving culture that has lasted for hundreds of years while developing a generally applicable and transferable knowledge base to increase its sharing and dissemination in the age of digitally supported production.
keywords	Cultural Heritage, Digital Fabrication, Craft Knowledge, Digital Craft, Analog-Digital
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_399
id	ecaade2022_399
authors	Johanes, Mikhael and Huang, Jeffrey
year	2022
title	Deep Learning Spatial Signature - Inverted GANs for Isovist representation in architectural floorplan
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. 621–629
doi	https://doi.org/10.52842/conf.ecaade.2022.2.621
summary	The advances of Generative Adversarial Networks (GANs) have provided a new experimental ground for creative architecture processes. However, the analytical potential of the latent representation of GANs is yet to be explored for architectural spatial analysis. Furthermore, most research on GANs for floorplan learning in architecture uses images as its main representation medium. This paper presents an experimental framework that uses one-dimensional periodic isovist samples and GANs inversion to recover its latent representation. Access to GANs’ latent space will open up a possibility for discriminative tasks such as classification and clustering analysis. The resulting latent representation will be investigated to discover its analytical capacity in extracting isovist spatial patterns from thousands of floorplans data. In this experiment, we hypothetically conclude that the spatial signature of the architectural floor plan could be derived from the degree of regularity of isovist samples in the latent space structure. The finding of this research will enable a new data-driven strategy to measure spatial quality using isovist and provide a new way for indexing architectural floorplan.
keywords	Machine Learning, Isovist, Latent Representation, GANs Inversion, Spatial Signature
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_279
id	caadria2022_279
authors	Kim, Dongyun, Guida, George and Garcia del Castillo y Lopez, Jose Luis
year	2022
title	PlacemakingAI : Participatory Urban Design with Generative Adversarial Networks
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
doi	https://doi.org/10.52842/conf.caadria.2022.2.485
summary	Machine Learning (ML) is increasingly present within the architectural discipline, expanding the current possibilities of procedural computer-aided design processes. Practical 2D design applications used within concept design stages are however limited by the thresholds of entry, output image fidelity, and designer agency. This research proposes to challenge these limitations within the context of urban planning and make the design processes accessible and collaborative for all urban stakeholders. We present PlacemakingAI, a design tool made to envision sustainable urban spaces. By converging supervised and unsupervised Generative Adversarial Networks (GANs) with a real-time user interface, the decision-making process of planning future urban spaces can be facilitated. Several metrics of walkability can be extracted from curated Google Street View (GSV) datasets when overlayed on existing street images. The contribution of this framework is a shift away from traditional design and visualization processes, towards a model where multiple design solutions can be rapidly visualized as synthetic images and iteratively manipulated by users. In this paper, we discuss the convergence of both a generative image methodology and this real-time urban prototyping and visualization tool, ultimately fostering engagement within the urban design process for citizens, designers, and stakeholders alike.
keywords	Machine Learning, Generative Adversarial Networks, user interface, real-time, walkability, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_316
id	caadria2022_316
authors	Ladron de Guevara, Manuel, Schneidman, Alexander, Byrne, Daragh and Krishnamurti, Ramesh
year	2022
title	Design Intents Disentanglement: A Multimodal Approach for Grounding Design Attributes in Objects
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. 333-342
doi	https://doi.org/10.52842/conf.caadria.2022.1.333
summary	Language is ambiguous; many terms and expressions convey the same idea. This is especially true in design fields, where conceptual ideas are generally described by high-level, qualitative attributes, called design intents. Words such as "organic", sequences like "this chair is a mixture between Japanese aesthetics and Scandinavian design" or more complex structures such as "we made the furniture layering materials like a bird weaving its nest‚ represent design intents. Furthermore, most design intents do not have unique visual representations, and are highly entangled within the design artifact, leading to complex relationships between language and images. This paper examines an alternative design scenario based on everyday natural language used by designers, where inputs such as a minimal and sleek looking chair are visually inferred by algorithms that have previously learned complex associations between designs and intents‚vision and language, respectively. We propose a multimodal sequence-to-sequence model which takes in design images and their corresponding descriptions and outputs a probability distribution over regions of the images in which design attributes are grounded. Expectedly, our model can reason and ground objective descriptors such as black or curved. Surprisingly, our model can reason about and ground more complex subjective attributes such as rippled or free, suggesting potential regions where the design object might register such vague descriptions. Link to code: https://github.com/manuelladron/codedBert.git
keywords	Natural Language Processing, Multimodal Machine Learning, Design Intents Disentanglement, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_281
id	caadria2022_281
authors	Moscovitz, Or and Barath, Shany
year	2022
title	A Generative Design Approach to Urban Sustainability Rating Systems During Early-Stage Urban Development
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. 171-180
doi	https://doi.org/10.52842/conf.caadria.2022.1.171
summary	Sustainability rating systems (SRS) aim to guide decision-makers in the planning process by defining clear guidelines and metrics. Nowadays, this process usually requires further tasks and the involvement of multiple professional advisors that eventually increase planning complexity and lead to lower SRS implementation. In this paper, we explore generative urban models and multi-objective optimization of SRS metrics to potentially enhance SRS use in planning processes. Furthermore, we apply this framework to a case study that has not reached its SRS planning goals due to contradicting trade-offs between municipal and stakeholder objectives. The urban model reflects the stakeholder design requirements and constraints such as the desired floor area ratio (FAR), building types, and units‚ number while the SRS metrics act as optimization goals. As part of the process, we automate quantitative indicators from Israel SRS ‚360 Neighbourhood‚ to use them as optimization goals and to analyse their correlation and trade-offs. Through this process, we enable a generative exploration of high-performing design iterations relative to a chosen set of SRS goals. Such a framework can enhance the integration of verified sustainability goals in the planning process, thus informing the stakeholders of their decision trade-off‚s concerning SRS indicators in urban development.
keywords	Sustainability Rating Systems, Generative Design, Multi-objective optimization, Urban Modelling and Simulation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220308
id	ijac202220308
authors	Rodrigues, Ricardo C; Rovenir B Duarte
year	2022
title	Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 630–644
summary	The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process
keywords	Dataset Reduction, Pix2pix, Artificial Intelligence, Deep Generative Models, GANs
series	journal
last changed	2024/04/17 14:30

_id	ecaade2022_272
id	ecaade2022_272
authors	Soman, Aditya, Azadi, Shervin and Nourian, Pirouz
year	2022
title	DeciGenArch - A generative design methodology for participatory architectural configuration via multi-criteria decision analysis
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 1, Ghent, 13-16 September 2022, pp. 459–468
doi	https://doi.org/10.52842/conf.ecaade.2022.1.459
summary	Our approach to Generative Design converts the problems of design from the geometrical drawing of shapes in a continuous setting to topological decision making about spatial configurations in a discrete setting. The paper presents a comprehensive formulation of the zoning problem as a sub-problem of architectural 3D layout configurations. This formulation focuses on the problem of zoning as a location-allocation problem in the context of Operations Research. Specifically, we propose a methodology for solving this problem by combining a well-known Multi-Criteria Decision-Analysis (MCDA) method called 'Technique for Order of Preference by Similarity to Ideal Solution' (TOPSIS) with a Multi-Agent System (MAS) operating in a discrete design space.
keywords	3D Layout problem, Participatory Design, Multi-Agent System, Multi-Criteria Decision Analysis, TOPSIS
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_53
id	sigradi2022_53
authors	Stuart-Smith, Robert; Danahy, Patrick
year	2022
title	3D Generative Design for Non-Experts: Multiview Perceptual Similarity with Agent-Based Reinforcement Learning
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 115–126
summary	Advances in additive manufacturing allow architectural elements to be fabricated with increasingly complex geometrical designs, however, corresponding 3D design software requires substantial knowledge and skill to operate, limiting adoption by non-experts or people with disabilities. Established non-expert approaches typically constrain geometry, topology, or character to a pre-established configuration, rather than aligning to figural and aesthetic characteristics defined by a user. A methodology is proposed that enables a user to develop multi-manifold designs from sketches or images in several 3d camera projections. An agent-based design approach responds to computer vision analysis (CVA) and Deep Reinforcement Learning (RL) to design outcomes with perceptual similarity to user input images evaluated by Structural Similarity Indexing (SSIM). Several CVA and RL ratios were explored in training models and tested on untrained images to evaluate their effectiveness. Results demonstrate a combination of CVA and RL motion behavior can produce meshes with perceptual similarity to image content.
keywords	Generative Design, Machine Learning, Agent-Based Systems, Non-Expert Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ascaad2022_102
id	ascaad2022_102
authors	Turki, Laila; Ben Saci, Abdelkader
year	2022
title	Generative Design for a Sustainable Urban Morphology
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. 434-449
summary	The present work concerns the applications of generative design for sustainable urban fabric. This represents an iterative process that involves an algorithm for the generation of solar envelopes to satisfy solar and density constraints. We propose in this paper to explore a meta-universe of human-machine interaction. It aims to design urban forms that offer solar access. This being to minimize heating energy expenditure and provide solar well-being. We propose to study the impact of the solar strategy of building morphosis on energy exposure. It consists of determining the layout and shape of the constructions based on the shading cut-off time. This is a period of desirable solar access. We propose to define it as a balance between the solar irradiation received in winter and that received in summer. We rely on the concept of the solar envelope defined since the 1970s by Knowles and its many derivatives (Koubaa Turki & al., 2020). We propose a parametric model to generate solar envelopes at the scale of an urban block. The generative design makes it possible to create a digital model of the different density solutions by varying the solar access duration. The virtual environment created allows exploring urban morphologies resilient both to urban densification and better use of the context’s resources. The seasonal energy balance, between overexposure in summer and access to the sun in winter, allows reaching high energy and environmental efficiency of the buildings. We have developed an algorithm on Dynamo for the generation of the solar envelope by shading exchange. The program makes it possible to detect the boundaries of the parcels imported from Revit, establish the layout of the building, and generate the solar envelopes for each variation of the shading cut-off time. It also calculates the FAR1 and the FSI2 from the variation of the shading cut-off time for each parcel of the island. We compare the solutions generated according to the urban density coefficients and the solar access duration. Once the optimal solution has been determined, we export the results back into Revit environment to complete the BIM modelling for solar study. This article proposes a method for designing buildings and neighbourhoods in a virtual environment. The latter acts upstream of the design process and can be extended to the different phases of the building life cycle: detailed design, construction, and use.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	caadria2022_420
id	caadria2022_420
authors	van Ameijde, Jeroen and Leung, Carson Ka Shut
year	2022
title	UAV-based People Location Tracking and Analysis for the Data-Driven Assessment of Social Activities in Public Spaces
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. 293-302
doi	https://doi.org/10.52842/conf.caadria.2022.1.293
summary	In sustainable high-density cities, public spaces play an important role in supporting social and community health and well-being. Amidst ongoing urbanisation, it is of increasing importance to study public space interaction patterns and placemaking processes that contribute to the quality of life of urban residents. This paper reports on the development of a new methodology for the computational tracking and analysis of social activities in urban spaces, using Computer Vision Object Detection (CVOD) techniques to create digitalised pedestrian trajectory data. Referring to concepts from humanistic geography and time geography, our method offers a new platform for data-driven urban place studies, detecting co-presence and social interaction in relation to urban morphology. This paper focuses on the development of Machine Learning protocols, algorithms for tracing and mapping pedestrian trajectories in a georeferenced photogrammetry model, and computational analysis of co-presence. The resulting workflow forms a foundation for future research around detecting, analysing and quantifying behavioural parameters, to evaluate the ability of public spaces to support social interaction and placemaking.
keywords	Public Space Analysis, Pedestrian Location Tracking, Computer Vision Object Detection, Machine Learning, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	cdrf2022_499
id	cdrf2022_499
authors	Yuxuan Wang, Yuran Liu, Riley Studebaker, Billie Faircloth, and Robert Stuart-Smith
year	2022
title	Ceramic Incremental Forming–A Rapid Mold-Less Forming Method of Variable Surfaces
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_43
summary	Following architectural practice’s widespread adoption of 3D modelling software, the digital design of free-form surfaces has enabled more heterogeneously organized architectural assemblies. However, fabricating envelope components with double-curved surface geometry have remained a challenge, involving significant machine time and material waste, and great expense to produce. This proof-of-concept project proposes a rapid, low-cost, and minimal-waste approach to forming double curved ceramic components through a novel approach to Ceramic Incremental Forming (CIF), using a 6-axis industrial robot, a passive flexible mold, and a custom ball-rolling tool. The approach is comparable to Single Point Incremental Forming (SPIF) that is used for forming complex shapes with metal sheets. This method promises to achieve high-quality, ceramic building envelope components, while eliminating the need to build proprietary molds for each shape and reducing the waste in the forming process. Compared with other architectural mold-less forming methods such as clay 3D printing, the approach is more time and material efficient, while being able to achieve similar levels of complexity. Thus, CIF may offer potential for further development and industrial applications.
series	cdrf
email
last changed	2024/05/29 14:03

_id	ecaade2022_234
id	ecaade2022_234
authors	Afsar, Secil, Estévez, Alberto T., Abdallah, Yomna K., Turhan, Gozde Damla, Ozel, Berfin and Doyuran, Aslihan
year	2022
title	Activating Co-Creation Methodologies of 3D Printing with Biocomposites Developed from Local Organic Wastes
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 1, Ghent, 13-16 September 2022, pp. 215–224
doi	https://doi.org/10.52842/conf.ecaade.2022.1.215
summary	Compared to the take-make-waste-oriented linear economy model, the circular model has been studied since the 1980s. Due to consumption-oriented lifestyles along with having a tendency of considering waste materials as trash, studies on sustainable materials management (SMM) have remained at a theoretical level or created temporary and limited impacts. To ensure SMM supports The European Green Deal, there is a necessity of developing top-down and bottom-up strategies simultaneously, which can be metaphorized as digging a tunnel from two different directions to meet in the middle of a mountain. In parallel with the New European Bauhaus concept, this research aims to create a case study for boosting bottom-up and data-driven methodologies to produce short-loop products made of bio-based biocomposite materials from local food & organic wastes. The Architecture departments of two universities from different countries collaborated to practice these design democratization methodologies using data transfer paths. The 3D printable models, firmware code, and detailed explanation of working with a customized 3D printer paste extruder were shared using online tools. Accordingly, the bio-based biocomposite recipe from eggshell, xanthan gum, and citric acid, which can be provided from local shops, food & organic wastes, was investigated concurrently to enhance its printability feature for generating interior design elements such as a vase or vertical gardening unit. While sharing each step from open-source platforms with adding snapshots and videos allows further development between two universities, it also makes room for other researchers/makers/designers to replicate the process/product. By combining modern manufacturing and traditional crafting methods with materials produced with DIY techniques from local resources, and using global data transfer platforms to transfer data instead of products themselves, this research seeks to unlock the value of co-creative design practices for SMM.
keywords	Sustainable Materials Management, Co-Creation, Food Waste, 3D Printing, New European Bauhaus
series	eCAADe
email
last changed	2024/04/22 07:10

_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

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