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	ecaadesigradi2019_034
id	ecaadesigradi2019_034
authors	Chen, Dechen, Luo, Dan, Xu, Weiguo, Luo, Chen, Shen, Liren, Yan, Xia and Wang, Tianjun
year	2019
title	Re-perceive 3D printing with Artificial Intelligence
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 443-450
doi	https://doi.org/10.52842/conf.ecaade.2019.1.443
summary	How can machine learning be combined with intelligent construction, material testing and other related topics to develop a new method of fabrication? This paper presents a set of experiments on the dynamic control of the heat deflection of thermoplastics in searching for a new 3D printing method with the dynamic behaviour of PLA and with a comprehensive workflow utilizing mechanic automation, computer vision, and artificial intelligence. Additionally, this paper will discuss in-depth the performance of different types of neural networks used in the research and conclude with solid data on the potential connection between the structure of neural networks and the dynamic, complex material performance we are attempting to capture.
keywords	3D printing; AI; automation; material; fabrication
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_514
id	ecaadesigradi2019_514
authors	de Miguel, Jaime, Villafa?e, Maria Eugenia, Piškorec, Luka and Sancho-Caparrini, Fernando
year	2019
title	Deep Form Finding - Using Variational Autoencoders for deep form finding of structural typologies
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 71-80
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
summary	In this paper, we are aiming to present a methodology for generation, manipulation and form finding of structural typologies using variational autoencoders, a machine learning model based on neural networks. We are giving a detailed description of the neural network architecture used as well as the data representation based on the concept of a 3D-canvas with voxelized wireframes. In this 3D-canvas, the input geometry of the building typologies is represented through their connectivity map and subsequently augmented to increase the size of the training set. Our variational autoencoder model then learns a continuous latent distribution of the input data from which we can sample to generate new geometry instances, essentially hybrids of the initial input geometries. Finally, we present the results of these computational experiments and lay out the conclusions as well as outlook for future research in this field.
keywords	artificial intelligence; deep neural networks; variational autoencoders; generative design; form finding; structural design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaadesigradi2019_605
id	ecaadesigradi2019_605
authors	Andrade Zandavali, Bárbara and Jiménez García, Manuel
year	2019
title	Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 217-226
doi	https://doi.org/10.52842/conf.ecaade.2019.3.217
summary	Brickwork is the oldest construction method still in use. Digital technologies, in turn, enabled new methods of representation and automation for bricklaying. While automation explored different approaches, representation was limited to declarative methods, as parametric filling algorithms. Alternatively, this work proposes a framework for automated brickwork using a machine learning model based on image-to-image translation (Conditional Generative Adversarial Networks). The framework consists of creating a dataset, training a model for each bond, and converting the output images into vectorial data for robotic assembly. Criteria such as: reaching wall boundary accuracy, avoidance of unsupported bricks, and brick's position accuracy were individually evaluated for each bond. The results demonstrate that the proposed framework fulfils boundary filling and respects overall bonding structural rules. Size accuracy demonstrated inferior performance for the scale tested. The association of this method with 'self-calibrating' robots could overcome this problem and be easily implemented for on-site.
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

_id	cf2019_048
id	cf2019_048
authors	Argota Sanchez-Vaquerizo, Javier and Daniel Cardoso Llach
year	2019
title	The Social Life of Small Urban Spaces 2.0 Three Experiments in Computational Urban Studies
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 430
summary	This paper introduces a novel framework for urban analysis that leverages computational techniques, along with established urban research methods, to study how people use urban public space. Through three case studies in different urban locations in Europe and the US, it demonstrates how recent machine learning and computer vision techniques may assist us in producing unprecedently detailed portraits of the relative influence of urban and environmental variables on people’s use of public space. The paper further discusses the potential of this framework to enable empirically-enriched forms of urban and social analysis with applications in urban planning, design, research, and policy.
keywords	Data Analytics, Urban Design, Machine Learning, Artificial Intelligence, Big Data, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:18

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2024_222
id	ecaade2024_222
authors	Bindreiter, Stefan; Sisman, Yosun; Forster, Julia
year	2024
title	Visualise Energy Saving Potentials in Settlement Development: By linking transport and energy simulation models for municipal planning
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 79–88
doi	https://doi.org/10.52842/conf.ecaade.2024.2.079
summary	To achieve Sustainable Development Goals, in addition to the switch to sustainable energy sources and energy-efficient buildings, transport offers a major lever for reducing energy consumption and greenhouse gases. The increasing demand for emission-free mobility (e.g. through electromobility) but also heat pumps has a direct impact on the electricity consumption of buildings and settlements. It is still difficult to simulate the effects and interactions of different measures as sector coupling concepts require comprehensible tools for ex ante evaluation of planning measures at the community level and the linking of domain-specific models (energy, transport). Using the municipality of Bruck an der Leitha (Austria) as an example, a digital twin based on an open data model (Bednar et al., 2020) is created for the development of methods, which can be used to simulate measures to improve the settlement structure within the municipality. Forecast models for mobility (Schmaus, 2019; Ritz, 2019) and the building stock are developed or applied and linked via the open data model to be able to run through development scenarios and variants. The forecasting and visualisation options created in the project form the basis for the ex-ante evaluation of measures and policies on the way to a Positive-Energy-District. By identifying and collecting missing data, data gaps are filled for the simulation of precise models in the specific study area. A digital, interactive 3D model is created to examine the forecast results and the different scenarios.
keywords	visualisation, decision support, sector coupling, holistic spatial energy models for municipal planning, (energy) saving potentials in settlement development
series	eCAADe
email
last changed	2024/11/17 22:05

_id	acadia19_412
id	acadia19_412
authors	Del Campo, Matias; Manninger, Sandra; Carlson, Alexandra
year	2019
title	Imaginary Plans
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 412-418
doi	https://doi.org/10.52842/conf.acadia.2019.412
summary	Artificial Neural Networks (NN) have become ubiquitous across disciplines due to their high performance in modeling the real world to execute complex tasks in the wild. This paper presents a computational design approach that uses the internal representations of deep vision neural networks to generate and transfer stylistic form edits to both 2D floor plans and building sections. The main aim of this paper is to demonstrate and interrogate a design technique based on deep learning. The discussion includes aspects of machine learning, 2D to 2D style transfers, and generative adversarial processes. The paper examines the meaning of agency in a world where decision making processes are defined by human/machine collaborations (Figure 1), and their relationship to aspects of a Posthuman design ecology. Taking cues from the language used by experts in AI, such as Hallucinations, Dreaming, Style Transfer, and Vision, the paper strives to clarify the position and role of Artificial Intelligence in the discipline of Architecture.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_353
id	ecaadesigradi2019_353
authors	Gönenç Sorguç, Arzu, Kruºa Yemiºcio?lu, Müge and Özgenel, Ça?lar F?rat
year	2019
title	A Computational Design Workshop Experience for 21st Century Architecture Education
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 127-136
doi	https://doi.org/10.52842/conf.ecaade.2019.1.127
summary	With the rapid increase in the accessible data, available information surpasses one's ability to extract knowledge from, which puts a great emphasis on the skills of the individual to reach and use relevant information, adapt to changing conditions and sustain respective skills. ICT skills, critical thinking skills, and communication/collaboration skills emerge as the survival skills and key factors for individuals to cope with the demands of the 21st-century. It is known that educational institutions have struggles in changing the curricula/teaching system in coping with the requirements of the rapidly evolving industry. Thus, workshops gained more importance in different levels which are a part of curricular or extracurricular activities to re-furnish existing skills or gain new skills. In the scope of this study, the learning and teaching approaches based on STEAM approach are assessed through a three-day workshop aiming to illustrate how these survival skills can be conveyed and embedded into the architecture education. The workshop is designed to be inclusive for all architecture students regardless of their level of education or background knowledge/skills. Within the scope of this paper, the conduction strategies of the workshop are covered in detail to highlight the importance of these survival skills along with the modes of teaching and share the best practices and gained knowledge for future works.
keywords	Computational Design Workshop; Architectural Education Strategies; Survival Skills
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:50

_id	caadria2019_109
id	caadria2019_109
authors	Kim, Jinsung, Song, Jaeyeol and Lee, Jin-Kook
year	2019
title	Approach to Auto-recognition of Design Elements for the Intelligent Management of Interior Pictures
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 785-794
doi	https://doi.org/10.52842/conf.caadria.2019.2.785
summary	This paper explores automated recognition of elements in interior design pictures for an intelligent design reference management system. Precedent design references have a significant role to help architects, designer and even clients in general architecture design process. Pictures are one of the representation that could exactly show a kind of design idea and knowledge. Due to the velocity, variety and volume of reference pictures data with growth of references platform, it is hard and time-consuming to handle the data with current manual way. To solve this problem , this paper depicts a deep learning-based approach to figuring out design elements and recognizing the design feature of them on the interior pictures using faster-RCNN and CNN algorithms. The targets are the residential furniture such as a table and a seating. Through proposed application, input pictures can automatically have tagging data as follows; seating1(type: sofa, seating capacity: two-seaters, design style: classic)
keywords	Interior design picture; Design element; Design feature; Automated recognition; Design Reference management
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaadesigradi2019_339
id	ecaadesigradi2019_339
authors	Kinugawa, Hina and Takizawa, Atsushi
year	2019
title	Deep Learning Model for Predicting Preference of Space by Estimating the Depth Information of Space using Omnidirectional Images
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 61-68
doi	https://doi.org/10.52842/conf.ecaade.2019.2.061
summary	In this study, we developed a method for generating omnidirectional depth images from corresponding omnidirectional RGB images of streetscapes by learning each pair of omnidirectional RGB and depth images created by computer graphics using pix2pix. Then, the models trained with different series of images shot under different site and weather conditions were applied to Google street view images to generate depth images. The validity of the generated depth images was then evaluated visually. In addition, we conducted experiments to evaluate Google street view images using multiple participants. We constructed a model that estimates the evaluation value of these images with and without the depth images using the learning-to-rank method with deep convolutional neural network. The results demonstrate the extent to which the generalization performance of the streetscape evaluation model changes depending on the presence or absence of depth images.
keywords	Omnidirectional image; depth image; Unity; Google street view; pix2pix; RankNet
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	caadria2019_362
id	caadria2019_362
authors	Lee, Jaejong, Ikeda, Yasushi and Hotta, Kensuke
year	2019
title	Comparative Evaluation of Viewing Elements by Visibility Heat Map of 3D Isovist - Urban planning experiment for Shinkiba in Tokyo Bay
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 341-350
doi	https://doi.org/10.52842/conf.caadria.2019.1.341
summary	This paper presents a visibility analysis for 3D urban environments and its possible applications for urban design. This multi-view visibility analysis tool was generated by 3D isovist in Grasshopper, Rhino. The advantage of this analysis tool is that it can be compared within the measurement area. In addition, setting a visual object different from the existing isovist. The visual object is a landmark of a city space, such as landscape or object. First, the application experimented on the relevance between the calculation time and precision by this analysis tool. Based on the results of this experiment, it applied it to an actual part of an urban space. The multi-view visibility includes confirming the possibility of a comprehensive evaluation on the urban redevelopment and change of the view caused by the building layout plan - by numerical analysis showing the visual characteristics of the area while using 3D isovist theory. The practically applied area is Shinkiba, which is a part of Tokyo's landfill site; and while using the calculated data, multi-view visibility of each plan in the simulation of the visibility map is compared and evaluated.
keywords	3D isovist; Multi-view visibility; Comprehensive integration visibility evaluation; Urban redevelopment; Algorithmic urban design
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	ecaade2024_92
id	ecaade2024_92
authors	Mayor Luque, Ricardo; Beguin, Nestor; Rizvi Riaz, Sheikh; Dias, Jessica; Pandey, Sneham
year	2024
title	Multi-material Gradient Additive Manufacturing: A data-driven performative design approach to multi-materiality through robotic fabrication
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. 381–390
doi	https://doi.org/10.52842/conf.ecaade.2024.1.381
summary	Buildings are responsible for 39% of global energy-related carbon emissions, with operational activities contributing 28% and materials and construction accounting for 11%(World Green Building Council, 2019) It is therefore vital to reconsider our reliance on fossil fuels for building materials and to develop new advanced manufacturing techniques that enable an integrated approach to material-controlled conception and production. The emergence of Multi-material Additive Manufacturing (MM-AM) technology represents a paradigm shift in producing elements with hybrid properties derived from novel and optimized solutions. Through robotic fabrication, MM-AM offers streamlined operations, reduced material usage, and innovative fabrication methods. It encompasses a plethora of methods to address diverse construction needs and integrates material gradients through data-driven analyses, challenging traditional prefabrication practices and emphasizing the current growth of machine learning algorithms in design processes. The research outlined in this paper presents an innovative approach to MM-AM gradient 3D printing through robotic fabrication, employing data-driven performative analyses enabling control over print paths for sustainable applications in both the AM industry and our built environment. The article highlights several designed prototypes from two distinct phases, demonstrating the framework's viability, implications, and constraints: a workshop dedicated to data-driven analyses in facade systems for MM-AM 3D-printed brick components, and a 3D-printed brick facade system utilizing two renewable and bio-materials—Cork sourced from recycled stoppers and Charcoal, with the potential for carbon sequestration.
keywords	Data-driven Performative design, Multi-material 3d Printing, Material Research, Fabrication-informed Material Design, Robotic Fabrication
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaadesigradi2019_135
id	ecaadesigradi2019_135
authors	Newton, David
year	2019
title	Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 21-28
doi	https://doi.org/10.52842/conf.ecaade.2019.2.021
summary	The field of generative architectural design has explored a wide range of approaches in the automation of design production, but these approaches have demonstrated limited artificial intelligence. Generative Adversarial Networks (GANs) are a leading deep generative model that use deep neural networks (DNNs) to learn from a set of training examples in order to create new design instances with a degree of flexibility and fidelity that outperform competing generative approaches. Their application to generative tasks in architecture, however, has been limited. This research contributes new knowledge on the use of GANs for architectural plan generation and analysis in relation to the work of specific architects. Specifically, GANs are trained to synthesize architectural plans from the work of the architect Le Corbusier and are used to provide analytic insight. Experiments demonstrate the efficacy of different augmentation techniques that architects can use when working with small datasets.
keywords	generative design; deep learning; artificial intelligence; generative adversarial networks
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	caadria2019_650
id	caadria2019_650
authors	Papasotiriou, Tania
year	2019
title	Identifying the Landscape of Machine Learning-Aided Architectural Design - A Term Clustering and Scientometrics Study
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 815-824
doi	https://doi.org/10.52842/conf.caadria.2019.2.815
summary	Recent advances in Machine Learning and Deep Learning revolutionise many industry disciplines and underpin new ways of problem-solving. This paradigm shift hasn't left Architecture unaffected. To investigate the impact on architectural design, this study utilises two approaches. First, a text mining method for content analysis is employed, to perform a robust review of the field's literature. This allows identifying and discussing current trends and possible future directions of this research domain in a systematic manner. Second, a Scientometrics study based on bibliometric reviews is employed to obtain quantitative measures of the global research activity in the described domain. Insights on research trends and identification of the most influential networks in this dataset were acquired by analysing terms co-occurrence, scientific collaborations, geographic distribution, and co-citation analysis. The paper concludes with a discussion on the limitations, opportunities and future research directions in the field of Machine Learning-aided architectural design.
keywords	Machine Learning; Text mining; Scientometrics
series	CAADRIA
email
last changed	2022/06/07 08:00

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

_id	acadia19_168
id	acadia19_168
authors	Adilenidou, Yota; Ahmed, Zeeshan Yunus; Freek, Bos; Colletti, Marjan
year	2019
title	Unprintable Forms
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp.168-177
doi	https://doi.org/10.52842/conf.acadia.2019.168
summary	This paper presents a 3D Concrete Printing (3DCP) experiment at the full scale of virtualarchitectural bodies developed through a computational technique based on the use of Cellular Automata (CA). The theoretical concept behind this technique is the decoding of errors in form generation and the invention of a process that would recreate the errors as a response to optimization (Adilenidou 2015). The generative design process established a family of structural and formal elements whose proliferation is guided through sets of differential grids (multi-grids) leading to the build-up of large span structures and edifices, for example, a cathedral. This tooling system is capable of producing, with specific inputs, a large number of outcomes in different scales. However, the resulting virtual surfaces could be considered as "unprintable" either due to their need of extra support or due to the presence of many cavities in the surface topology. The above characteristics could be categorized as errors, malfunctions, or undesired details in the geometry of a form that would need to be eliminated to prepare it for printing. This research project attempts to transform these "fabrication imprecisions" through new 3DCP techniques into factors of robustness of the resulting structure. The process includes the elimination of the detail / "errors" of the surface and their later reinsertion as structural folds that would strengthen the assembly. Through this process, the tangible outputs achieved fulfill design and functional requirements without compromising their structural integrity due to the manufacturing constraints.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:54

_id	cf2019_037
id	cf2019_037
authors	Aljammaz, Mohammed ; Tsung-Hsien Wang and Chengzhi Peng
year	2019
title	The influence of Saudi Arabian culture on energy use: Improving the time-use schedules in energy simulation for houses in Riyadh
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 273-289
summary	Culture influences the way that people act and behave in all societies. In Saudi Arabia, culture and beliefs directly influence the lifestyle and behaviour of its citizens. Culture also impacts on energy usage of buildings, but this factor is often excluded from energy use simulations. A consequence of this is a mismatch between energy prediction and real energy usage. This paper demonstrates how a time-use data (TUD) model can be used to create a more realistic estimate of energy consumption in Saudi Arabia. TUD has been collected through a survey of 300 people living in Riyadh. The performance of the computational TUD model is cross-referenced with empirical data and the outcomes are used to discuss how the TUD model can be applied more effectively in energy use simulations.
keywords	time-use data, energy simulation, energy use prediction, load schedules, occupant behaviours,
series	CAAD Futures
email
last changed	2019/07/29 14:15

_id	ecaadesigradi2019_061
id	ecaadesigradi2019_061
authors	Alkadri, Miktha Farid, De Luca, Francesco, Turrin, Michela and Sariyildiz, Sevil
year	2019
title	Making use of Point Cloud for Generating Subtractive Solar Envelopes
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 633-640
doi	https://doi.org/10.52842/conf.ecaade.2019.1.633
summary	As a contextual and passive design strategy, solar envelopes play a great role in determining building mass based on desirable sun access during the predefined period. With the rapid evolution of digital tools, the design method of solar envelopes varies in different computational platforms. However, current approaches still lack in covering the detailed complex geometry and relevant information of the surrounding context. This, consequently, affects missing information during contextual analysis and simulation of solar envelopes. This study proposes a subtractive method of solar envelopes by considering the geometrical attribute contained in the point cloud of TLS (terrestrial laser scanner) dataset. Integration of point cloud into the workflow of solar envelopes not only increases the robustness of final geometry of existing solar envelopes but also enhances awareness of architects during contextual analysis due to consideration of surface properties of the existing environment.
keywords	point cloud data; solar envelopes; subtractive method; solar access
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

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