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	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	caadria2019_452
id	caadria2019_452
authors	Choi, Minkyu, Yi, Taeha, Kim, Meereh and Lee, Ji-Hyun
year	2019
title	Land Price Prediction System Using Case-based Reasoning
doi	https://doi.org/10.52842/conf.caadria.2019.1.767
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. 767-774
summary	Real estate price prediction is very complex process. Big data and machine learning technology have been introduced in many research areas, and they are also making such an attempt in the real estate market. Although real estate price forecasting studies is actively conducted, using support vector machine, machine learning algorithm, AHP method, and so on, validity and accuracy are still not reliable.In this research, we propose a Case-Based Reasoning system using regression analysis to allocate weight of attributes. This proposed system can support to predict the real estate price based on collecting public data and easily update the knowledge about real estate. Since the result shows error rate less than 30% through the experiment, this algorithm gives better performance than previous one. By this research, it is possible for help decision-makers to expect the real estate price of interested area.
keywords	Artificial intelligence; Case-based reasoning; Land price prediction; Regression
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cf2019_032
id	cf2019_032
authors	Khean, Nariddh; Alessandra Fabbri, David Gerber and M. Hank Haeusler
year	2019
title	Examining Potential Socio-economic Factors that Aﬀect Machine Learning Research in the AEC Industry
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 254
summary	Machine learning (ML) has increasingly dominated discussions about the shape of mankind’s future, permeating almost all facets of our digital, and even physical, world. Yet, contrary to the relentless march of almost all other industries, the architecture, engineering and construction (AEC) industry have lagged behind in the uptake of ML for its own challenges. Through a systematic review of ML projects from a leading global engineering ﬁrm, this paper investigates social, political, economic, and cultural (SPEC) factors that have helped or hindered ML’s uptake. Further, the paper discusses how ML is perceived at various points in the economic hierarchy, how eﬀective forms of communication is vital in a highly-specialized workforce, and how ML’s unexpected eﬀectiveness have forced policy makers to reassess data governance and privacy; all the while considering what this means for the adoption of ML in the AEC industry. This investigation, its methodology, background research, systematic review, and its conclusion are presented.
keywords	Machine learning · Artiﬁcial intelligence · Research and development · Architecture, engineering, and construction industry · Social factors · Political factors · Economic factors · Cultural factors
series	CAAD Futures
type	normal paper
email
last changed	2019/07/29 14:17

_id	caadria2019_638
id	caadria2019_638
authors	Willemse, Elias Jakobus, Tuncer, Bige and Bouffanais, Roland
year	2019
title	Identifying Highly Dense Areas from Raw Location Data
doi	https://doi.org/10.52842/conf.caadria.2019.2.805
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. 805-814
summary	In this paper we show how very high-volumes of raw WiFi-based location data of individuals can be used to identify dense activity locations within a neighbourhood. Key to our methods is the inference of the size of the area directly from the data, without having to use additional geographical information. To extract the density information, data-mining and machine learning techniques form activity-based transportation modelling are applied. These techniques are demonstrated on data from a large-scale experiment conducted in Singapore in which tens of thousands of school children carried a multi-sensor device for five consecutive days. By applying the techniques we were able to identify expected high-density areas of school pupils, specifically their school locations, using only the raw data, demonstrating the general applicability of the methods.
keywords	; Machine Learning, Big-data, Location-analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_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
doi	https://doi.org/10.52842/conf.ecaade.2019.3.217
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
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	ijac201917106
id	ijac201917106
authors	Brown, Nathan C. and Caitlin T. Mueller
year	2019
title	Design variable analysis and generation for performance-based parametric modeling in architecture
source	International Journal of Architectural Computing vol. 17 - no. 1, 36-52
summary	Many architectural designers recognize the potential of parametric models as a worthwhile approach to performance- driven design. A variety of performance simulations are now possible within computational design environments, and the framework of design space exploration allows users to generate and navigate various possibilities while considering both qualitative and quantitative feedback. At the same time, it can be difficult to formulate a parametric design space in a way that leads to compelling solutions and does not limit flexibility. This article proposes and tests the extension of machine learning and data analysis techniques to early problem setup in order to interrogate, modify, relate, transform, and automatically generate design variables for architectural investigations. Through analysis of two case studies involving structure and daylight, this article demonstrates initial workflows for determining variable importance, finding overall control sliders that relate directly to performance and automatically generating meaningful variables for specific typologies.
keywords	Parametric design, design space formulation, data analysis, design variables, dimensionality reduction
series	journal
email
last changed	2019/08/07 14:04

_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
doi	https://doi.org/10.52842/conf.ecaade.2019.1.443
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
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	cf2019_021
id	cf2019_021
authors	Cheng, Chi-Li and June-Hao Hou
year	2019
title	A Method of Mesh Simplification for Drone 3D Modeling with Architectural Feature Extraction
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 169
summary	This paper proposes a method of mesh simplification for 3D terrain or city models generated photogrammetrically from drone captured images, enabled by the ability of extracting the architectural features. Compare to traditional geometric computational method, the proposed method recognizes and processes the features from the architectural perspectives. In addition, the workflow also allows exporting the simplified models and geometric features to open platforms, e.g. OpenStreetMap, for practical usages in site analysis, city generation, and contributing to the open data communities.
keywords	Mesh Reconstruction, photogrammetry, mesh simplification, procedural mode, machine learning
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	ijac201917102
id	ijac201917102
authors	Cutellic, Pierre
year	2019
title	Towards encoding shape features with visual event-related potential based brain–computer interface for generative design
source	International Journal of Architectural Computing vol. 17 - no. 1, 88-102
summary	This article will focus on abstracting and generalising a well-studied paradigm in visual, event-related potential based brain–computer interfaces, for the spelling of characters forming words, into the visually encoded discrimination of shape features forming design aggregates. After identifying typical technologies in neuroscience and neuropsychology of high interest for integrating fast cognitive responses into generative design and proposing the machine learning model of an ensemble of linear classifiers in order to tackle the challenging features that electroencephalography data carry, it will present experiments in encoding shape features for generative models by a mechanism of visual context updating and the computational implementation of vision as inverse graphics, to suggest that discriminative neural phenomena of event-related potentials such as P300 may be used in a visual articulation strategy for modelling in generative design.
keywords	Generative design, machine learning, brain–computer interface, design computing and cognition, integrated cognition, neurodesign, shape, form and geometry, design concepts and strategies
series	journal
email
last changed	2019/08/07 14:04

_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
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
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
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	cf2019_014
id	cf2019_014
authors	Ferrando, Cecilia; Niccolo Dalmasso, Jiawei Mai, Daniel Cardoso Llach
year	2019
title	Architectural Distant Reading Using Machine Learning to Identify Typological Traits Across Multiple Buildings
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 114-127
summary	This paper introduces an approach to architectural “distant reading”: the use of computational methods to analyze architectural data in order to derive spatial insights from—and explore new questions concerning—large collections of architectural work. Through a case study comprising a dataset of religious buildings, we show how we may use machine learning techniques to identify typological and functional traits from building plans. We find that spatial structure, rather than local features, is particularly effective in supporting this type of analysis. Further, we speculate on the potential of this computational method to enrich architectural design, research, and criticism by, for example, enabling new ways of thinking about architectural concepts such as typology in ways that reflect gradual variations, rather than sharp distinctions.
keywords	Architectural Analytics, Machine Learning, Classification, Religious buildings, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	ecaadesigradi2019_510
id	ecaadesigradi2019_510
authors	Giannopoulou, Effima, Baquero, Pablo, Warang, Angad, Orciuoli, Affonso and T. Estévez, Alberto
year	2019
title	Stripe Segmentation for Branching Shell Structures - A Data Set Development as a Learning Process for Fabrication Efficiency and Structural Performance
doi	https://doi.org/10.52842/conf.ecaade.2019.3.063
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. 63-70
summary	This article explains the evolution towards the subject of digital fabrication of thin shell structures, searching for the computational design techniques which allow to implement biological pattern mechanisms for efficient fabrication procedures. The method produces data sets in order to analyse and evaluate parallel alternatives of branching topologies, segmentation patterns, material usage, weight and deflection values as a user learning process. The importance here is given to the selection of the appropriate attributes, referring to which specific geometric characteristics of the parametric model are affecting each other and with what impact. The outcomes are utilized to train an Artificial Neural Network to predict new building information based on new combinations of desired parameters so that the user can decide and adjust the design based on the new information.
keywords	Digital Fabrication; Shell Structures; Segmentation; Machine Learning; Branching Topologies; Bio-inspired
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:51

_id	ecaadesigradi2019_671
id	ecaadesigradi2019_671
authors	Jabi, Wassim, Chatzivasileiadi, Aikaterini, Wardhana, Nicholas Mario, Lannon, Simon and Aish, Robert
year	2019
title	The synergy of non-manifold topology and reinforcement learning for fire egress
doi	https://doi.org/10.52842/conf.ecaade.2019.2.085
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. 85-94
summary	This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.
keywords	Non-manifold topology; Topologic; Reinforcement Learning; Fire egress
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	cf2019_022
id	cf2019_022
authors	Koh, Immanuel and Jeffrey Huang
year	2019
title	Citizen Visual Search Engine:Detection and Curation of Urban Objects
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 170
summary	Increasingly, the ubiquity of satellite imagery has made the data analysis and machine learning of large geographical datasets one of the building blocks of visuospatial intelligence. It is the key to discover current (and predict future) cultural, social, financial and political realities. How can we, as designers and researchers, empower citizens to understand and participate in the design of our cities amid this technological shift? As an initial step towards this broader ambition, a series of creative web applications, in the form of visual search engines, has been developed and implemented to data mine large datasets. Using open sourced deep learning and computer vision libraries, these applications facilitate the searching, detecting and curating of urban objects. In turn, the paper proposes and formulates a framework to design truly citizen-centric creative visual search engines -- a contribution to citizen science and citizen journalism in spatial terms.
keywords	Deep Learning, Computer Vision, Satellite Imagery, Citizen Science, Artificial Intelligence
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	acadia19_664
id	acadia19_664
authors	Koshelyuk, Daniil; Talaei, Ardeshir; Garivani, Soroush; Markopoulou, Areti; Chronis, Angelo; Leon, David Andres; Krenmuller, Raimund
year	2019
title	Alive
doi	https://doi.org/10.52842/conf.acadia.2019.664
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. 664-673
summary	In the context of data-driven culture, built space still maintains low responsiveness and adaptability. Part of this reality lies in the low resolution of live information we have about the behavior and condition of surfaces and materials. This research addresses this issue by exploring the development of a deformation-sensing composite membrane material system following a bottom-up approach and combining various technologies toward solving related technical issues—exploring conductivity properties of graphene and maximizing utilization within an architecture-related proof-of-concept scenario and a workflow including design, fabrication, and application methodology. Introduced simulation of intended deformation helps optimize the pattern of graphene nanoplatelets (GNP) to maximize membrane sensitivity to a specific deformation type while minimizing material usage. Research explores various substrate materials and graphene incorporation methods with initial geometric exploration. Finally, research introduces data collection and machine learning techniques to train recognition of certain types of deformation (single point touch) on resistance changes. The final prototype demonstrates stable and symmetric readings of resistance in a static state and, after training, exhibits an 88% prediction accuracy of membrane shape on a labeled sample data-set through a pre-trained neural network. The proposed framework consisting of a simulation based, graphene-capturing fabrication method on stretchable surfaces, and includes initial exploration in neural network training shape detection, which combined, demonstrate an advanced approach to embedding intelligence.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:51

_id	acadia19_404
id	acadia19_404
authors	Liu, Henan; Liao, Longtai; Srivastava, Akshay
year	2019
title	AN ANONYMOUS COMPOSITION
doi	https://doi.org/10.52842/conf.acadia.2019.404
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. 404-411
summary	Within the context of continuous technology transformations, the way scientists and designers process data is changing dramatically from simplification and explicit defined rules to searching and retrieving. Ideally, such a trending method can eliminate issues including deviation and ambiguity with the help of hypothetically unlimited computational power. To process data in this manner, artificial intelligence is necessary and needs to be integrated into the design process. An experiment of a design process that consists of a generative model, a data library, and a machine learning system (GAN) is introduced to demonstrate its effectiveness. The methodology is further evaluated by comparing its output with its input targets, which proves the possibility of employing machine learning systems to aggressively process data and automate the design process. Further improvement of such methodology, including judging criteria and possible applications, and the sensibility of the machine is also discussed at the end.
keywords	Machine Learning, Automation, Variables, Data Processing, Sensibility, Generative Design
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:59

_id	sigradi2023_416
id	sigradi2023_416
authors	Machado Fagundes, Cristian Vinicius, Miotto Bruscato, Léia, Paiva Ponzio, Angelica and Chornobai, Sara Regiane
year	2023
title	Parametric environment for internalization and classification of models generated by the Shap-E tool
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1689–1698
summary	Computing has been increasingly employed in design environments, primarily to perform calculations and logical decisions faster than humans could, enabling tasks that would be impossible or too time-consuming to execute manually. Various studies highlight the use of digital tools and technologies in diverse methods, such as parametric modeling and evolutionary algorithms, for exploring and optimizing alternatives in architecture, design, and engineering (Martino, 2015; Fagundes, 2019). Currently, there is a growing emergence of intelligent models that increasingly integrate computers into the design process. Demonstrating great potential for initial ideation, artificial intelligence (AI) models like Shap-E (Nichol et al., 2023) by OpenAI stand out. Although this model falls short of state-of-the-art sample quality, it is among the most efficient orders of magnitude for generating three-dimensional models through AI interfaces, offering practical balance for certain use cases. Thus, aiming to explore this gap, the presented study proposes an innovative design agency framework by employing Shap-E connected with parametric modeling in the design process. The generation tool has shown promising results; through generations of synthetic views conditioned by text captions, its final output is a mesh. However, due to the lack of topological information in models generated by Shap-E, we propose to fill this gap by transferring data to a parametric three-dimensional surface modeling environment. Consequently, this interaction's use aims to enable the transformation of the mesh into quantifiable surfaces, subject to collection and optimization of dimensional data of objects. Moreover, this work seeks to enable the creation of artificial databases through formal categorization of parameterized outputs using the K-means algorithm. For this purpose, the study methodologically orients itself in a four-step exploratory experimental process: (1) creation of models generated by Shap-E in a pressing manner; (2) use of parametric modeling to internalize models into the Grasshopper environment; (3) generation of optimized alternatives using the evolutionary algorithm (Biomorpher); (4) and classification of models using the K-means algorithm. Thus, the presented study proposes, through an environment of internalization and classification of models generated by the Shap-E tool, to contribute to the construction of a new design agency methodology in the decision-making process of design. So far, this research has resulted in the generation and classification of a diverse set of three-dimensional shapes. These shapes are grouped for potential applications in machine learning, in addition to providing insights for the refinement and detailed exploration of forms.
keywords	Shap-E, Parametric Design, Evolutionary Algorithm, Synthetic Database, Artificial Intelligence
series	SIGraDi
email
last changed	2024/03/08 14:09

_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
doi	https://doi.org/10.52842/conf.ecaade.2024.1.381
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
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	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
doi	https://doi.org/10.52842/conf.caadria.2019.2.815
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
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	ecaadesigradi2019_550
id	ecaadesigradi2019_550
authors	Rhee, Jinmo, Cardoso Llach, Daniel and Krishnamurti, Ramesh
year	2019
title	Context-rich Urban Analysis Using Machine Learning - A case study in Pittsburgh, PA
doi	https://doi.org/10.52842/conf.ecaade.2019.3.343
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. 343-352
summary	This paper reports on the analytical potential of machine learning methods for urban analysis. It documents a new method for data-driven urban analysis based on diagrammatic images describing each building in a city in relation to its immediate urban context. By statistically analyzing architectural and contextual features in this new dataset, the method can identify clusters of similar urban conditions and produce a detailed picture of a city's morphological structure. Remapping the clusters from data to 2D space, our method enables a new kind of urban plan that displays gradients of urban similarity. Taking Pittsburgh as a case study we demonstrate this method, and propose "morphological types" as a new category of urban analysis describing a given city's specific set of distinct morphological conditions. The paper concludes with a discussion of the implications of this method and its limitations, as well as its potentials for architecture, urban studies, and computation.
keywords	Urban Morphology; Machine Learning; Architectural Contexts; Urban Analysis; GIS
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

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