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

PDF papers
References

Hits 1 to 20 of 652

Reformat results as: short short into frame detailed detailed into frame

_id	ecaade2020_515
id	ecaade2020_515
authors	Chadha, Kunaljit, Dubor, Alexandre, Puigpinos, Laura and Rafols, Irene
year	2020
title	Space Filling Curves for Optimising Single Point Incremental Sheet Forming using Supervised Learning Algorithms
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 555-562
doi	https://doi.org/10.52842/conf.ecaade.2020.1.555
summary	Increasing use of computational design tools have led to an increase in the demand for mass customised fabrication, rendering decades old industrial CAD-CAM protocols limiting for such fabrication processes. This bespoke demand of components has led to a unified workflow between design strategies and production techniques. Recent advances in computation have allowed us to predict and register the tolerances of fabrication before and while being fabricated. Procedural algorithms are a set of novel problem-solving methods and have been attracting considerable attention for their good performance.They follow a procedural way of iteration with an established way of behavior.In the particular case of Incremental Sheet forming (ISF), these algorithms can realize several functions such as edge detection and segmentation required for optimizing machining time and accuracy.In this context, this paper presents a methodology to optimize long-drawn-out ISF operation by using geometrical intervention informed by supervised machine learning algorithms.
keywords	Procedural Algorithms; Incremental Sheet Forming; Robotic Cold forming; Mass Customization
series	eCAADe
email
last changed	2022/06/07 07:55

_id	cdrf2019_93
id	cdrf2019_93
authors	Jiaxin Zhang , Tomohiro Fukuda , and Nobuyoshi Yabuki
year	2020
title	A Large-Scale Measurement and Quantitative Analysis Method of Façade Color in the Urban Street Using Deep Learning
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_9
summary	Color planning has become a significant issue in urban development, and an overall cognition of the urban color identities will help to design a better urban environment. However, the previous measurement and analysis methods for the facade color in the urban street are limited to manual collection, which is challenging to carry out on a city scale. Recent emerging dataset street view image and deep learning have revealed the possibility to overcome the previous limits, thus bringing forward a research paradigm shift. In the experimental part, we disassemble the goal into three steps: firstly, capturing the street view images with coordinate information through the API provided by the street view service; then extracting facade images and cleaning up invalid data by using the deep-learning segmentation method; finally, calculating the dominant color based on the data on the Munsell Color System. Results can show whether the color status satisfies the requirements of its urban plan for façade color in the street. This method can help to realize the refined measurement of façade color using open source data, and has good universality in practice.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_113
id	ecaade2020_113
authors	Li, Yunqin, Yabuki, Nobuyoshi, Fukuda, Tomohiro and Zhang, Jiaxin
year	2020
title	A big data evaluation of urban street walkability using deep learning and environmental sensors - a case study around Osaka University Suita campus
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 319-328
doi	https://doi.org/10.52842/conf.ecaade.2020.2.319
summary	Although it is widely known that the walkability of urban street plays a vital role in promoting street quality and public health, there is still no consensus on how to measure it quantitatively and comprehensively. Recent emerging deep learning and sensor network has revealed the possibility to overcome the previous limit, thus bringing forward a research paradigm shift. Taking this advantage, this study explores a new approach for urban street walkability measurement. In the experimental study, we capture Street View Picture, traffic flow data, and environmental sensor data covering streets within Osaka University and conduct both physical and perceived walkability evaluation. The result indicates that the street walkability of the campus is significantly higher than that of municipal, and the streets close to large service facilities have better walkability, while others receive lower scores. The difference between physical and perceived walkability indicates the feasibility and limitation of the auto-calculation method.
keywords	walkability; WalkScore; deep learning; Street view picture; environmental sensor
series	eCAADe
email
last changed	2022/06/07 07:51

_id	cdrf2019_134
id	cdrf2019_134
authors	Zhen Han, Wei Yan, and Gang Liu
year	2020
title	A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_13
summary	In recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ijac202321102
id	ijac202321102
authors	Özerol, Gizem; Semra Arslan Selçuk
year	2023
title	Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 1, pp. 23–41
summary	Abstract Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.
keywords	Artificial intelligence, machine learning, deep learning, architectural research, bibliometric analysis
series	journal
last changed	2024/04/17 14:30

_id	cdrf2019_199
id	cdrf2019_199
authors	Ana Herruzo and Nikita Pashenkov
year	2020
title	Collection to Creation: Playfully Interpreting the Classics with Contemporary Tools
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_19
summary	This paper details an experimental project developed in an academic and pedagogical environment, aiming to bring together visual arts and computer science coursework in the creation of an interactive installation for a live event at The J. Paul Getty Museum. The result incorporates interactive visuals based on the user’s movements and facial expressions, accompanied by synthetic texts generated using machine learning algorithms trained on the museum’s art collection. Special focus is paid to how advances in computing such as Deep Learning and Natural Language Processing can contribute to deeper engagement with users and add new layers of interactivity.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_130
id	acadia20_130
authors	Newton, David
year	2020
title	Anxious Landscapes
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 130-137.
doi	https://doi.org/10.52842/conf.acadia.2020.2.130
summary	Advances in the field of machine learning over the last decade have revolutionized artificial intelligence by providing a flexible means to build analytic, predictive, and generative models from large datasets, but the allied design disciplines have yet to apply these tools at the urban level to draw analytic insights on how the built environment might impact human health. Previous research has found numerous correlations between the built environment and both physical and mental health outcomes—suggesting that the design of our cities may have significant impacts on human health. Developing methods of analysis that can provide insight on the correlations between the built environment and human health could help the allied design disciplines shape our cities in ways that promote human health. This research addresses these issues and contributes knowledge on the use of deep learning (DL) methods for urban analysis and mental health, specifically anxiety. Mental health disorders, such as anxiety, have been estimated to account for the largest proportion of global disease burden. The methods presented allow architects, planners, and urban designers to make use of large remote-sensing datasets (e.g., satellite and aerial images) for design workflows involving analysis and generative design tasks. The research also contributes insight on correlations between anxiety prevalence and specific urban design features—providing actionable intelligence for the planning and design of the urban fabric.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 228-236.
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_222
id	ecaade2020_222
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Automatic Generation of Horizontal Building Mask Images by Using a 3D Model with Aerial Photographs for Deep Learning
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 271-278
doi	https://doi.org/10.52842/conf.ecaade.2020.2.271
summary	Information extracted from aerial photographs is widely used in urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning for understanding the current state of a target region. However, the building mask images used to train the deep learning model are manually generated in many cases. To solve this challenge, a method has been proposed for automatically generating mask images by using virtual reality 3D models for deep learning. Because normal virtual models do not have the realism of a photograph, it is difficult to obtain highly accurate detection results in the real world even if the images are used for deep learning training. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D models with textured aerial photographs for deep learning. The model trained on datasets generated by the proposed method could detect buildings in aerial photographs with an accuracy of IoU = 0.622. Work left for the future includes changing the size and type of mask images, training the model, and evaluating the accuracy of the trained model.
keywords	Urban planning and design; Deep learning; Semantic segmentation; Mask image; Training data; Automatic design
series	eCAADe
email
last changed	2022/06/07 07:50

_id	caadria2020_088
id	caadria2020_088
authors	Kado, Keita, Furusho, Genki, Nakamura, Yusuke and Hirasawa, Gakuhito
year	2020
title	rocess Path Derivation Method for Multi-Tool Processing Machines Using Deep-Learning-Based Three Dimensional Shape Recognition
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. 609-618
doi	https://doi.org/10.52842/conf.caadria.2020.2.609
summary	When multi-axis processing machines are employed for high-mix, low-volume production, they are operated using a dedicated computer-aided design/ computer-aided manufacturing (CAD/CAM) process that derives an operating path concurrently with detailed modeling. This type of work requires dedicated software that occasionally results in complicated front-loading and data management issues. We proposed a three-dimensional (3D) shape recognition method based on deep learning that creates an operational path from 3D part geometry entered by a CAM application to derive a path for processing machinery such as a circular saw, drill, or end mill. The methodology was tested using 11 joint types and five processing patterns. The results show that the proposed method has several practical applications, as it addresses wooden object creation and may also have other applications.
keywords	Three-dimensional Shape Recognition; Deep Learning; Digital Fabrication; Multi-axis Processing Machine
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	acadia20_120
id	acadia20_120
authors	Barsan-Pipu, Claudiu; Sleiman, Nathalie; Moldovan, Theodor
year	2020
title	Affective Computing for Generating Virtual Procedural Environments Using Game Technologies
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 120-129.
doi	https://doi.org/10.52842/conf.acadia.2020.2.120
summary	Architects have long sought to create spaces that can relate to or even induce specific emotional conditions in their users, such as states of relaxation or engagement. Dynamic or calming qualities were given to these spaces by controlling form, perspective, lighting, color, and materiality. The actual impact of these complex design decisions has been challenging to assess, from both quantitative and qualitative standpoints, because neural empathic responses, defined in this paper by feature indexes (FIs) and mind indexes (MIs), are highly subjective experiences. Recent advances in the fields of virtual procedural environments (VPEs) and virtual reality (VR), supported by powerful game engine (GE) technologies, provide computational designers with a new set of design instruments that, when combined with brain-computing interfacing (BCI) and eye-tracking (E-T) hardware, can be used to assess complex empathic reactions. As the COVID-19 health crisis showed, virtual social interaction becomes increasingly relevant, and the social catalytic potential of VPEs can open new design possibilities. The research presented in this paper introduces the cyber-physical design of such an affective computing system. It focuses on how relevant empathic data can be acquired in real time by exposing subjects within a dynamic VR-based VPE and assessing their emotional responses while controlling the actual generative parameters via a live feedback loop. A combination of VR, BCI, and E-T solutions integrated within a GE is proposed and discussed. By using a VPE inside a BCI system that can be accurately correlated with E-T, this paper proposes to identify potential morphological and lighting factors that either alone or combined can have an empathic effect expressed by the relevant responses of the MIs.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2017_069
id	sigradi2017_069
authors	Briones Lazo, Carolina; Carolina Soto Ogueta
year	2017
title	La enseñanza de BIM en Chile, el desafío de un cambio de enfoque centrado en la metodología por sobre la tecnología. [BIM education in Chile, the challenge of a shift of focus centered on methodology over technology.]
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.470-478
summary	This article presents the level of adoption of BIM in Chile referring to recent studies carried out in the country, demonstrating that there has not been a significant increase in the use of this methodology by the industry. According to the analysis of international cases on educational frameworks, the authors argue that the development of a national education strategy for BIM with a focus on defining BIM capabilities required to assume the national mandate 2020, along with promoting collaborative work environments and active learning methodologies would be very beneficial.
keywords	Building Information Modelling; Metodología BIM; Adopción de BIM; Estrategia de enseñanza de BIM.
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	ecaade2023_99
id	ecaade2023_99
authors	Dervishaj, Arlind, Fonsati, Arianna, Hernández Vargas, José and Gudmundsson, Kjartan
year	2023
title	Modelling Precast Concrete for a Circular Economy in the Built Environment
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. 177–186
doi	https://doi.org/10.52842/conf.ecaade.2023.2.177
summary	In recent years, there has been a growing interest in adopting circular approaches in the built environment, specifically reusing existing buildings or their components in new projects. To achieve this, drawings, laser scanning, photogrammetry and other techniques are used to capture data on buildings and their materials. Although previous studies have explored scan-to-BIM workflows, automation of 2D drawings to 3D models, and machine learning for identifying building components and materials, a significant gap remains in refining this data into the right level of information required for digital twins, to share information and for digital collaboration in designing for reuse. To address this gap, this paper proposes digital guidelines for reusing precast concrete based on the level of information need (LOIN) standard EN 17412-1:2020 and examines several CAD and BIM modelling strategies. These guidelines can be used to prepare digital templates that become digital twins of existing elements, develop information requirements for use cases, and facilitate data integration and sharing for a circular built environment.
keywords	building information modelling (BIM), circular construction, reuse, concrete
series	eCAADe
email
last changed	2023/12/10 10:49

_id	cdrf2019_159
id	cdrf2019_159
authors	Hang Zhang and Ye Huang
year	2020
title	Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_15
summary	In the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_574
id	acadia20_574
authors	Nguyen, John; Peters, Brady
year	2020
title	Computational Fluid Dynamics in Building Design Practice
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 574-583.
doi	https://doi.org/10.52842/conf.acadia.2020.1.574
summary	This paper provides a state-of-the-art of computational fluid dynamics (CFD) in the building industry. Two methods were used to find this new knowledge: a series of interviews with leading architecture, engineering, and software professionals; and a series of tests in which CFD software was evaluated using comparable criteria. The paper reports findings in technology, workflows, projects, current unmet needs, and future directions. In buildings, airflow is fundamental for heating and cooling, as well as occupant comfort and productivity. Despite its importance, the design of airflow systems is outside the realm of much of architectural design practice; but with advances in digital tools, it is now possible for architects to integrate air flow into their building design workflows (Peters and Peters 2018). As Chen (2009) states, “In order to regulate the indoor air parameters, it is essential to have suitable tools to predict ventilation performance in buildings.” By enabling scientific data to be conveyed in a visual process that provides useful analytical information to designers (Hartog and Koutamanis 2000), computer performance simulations have opened up new territories for design “by introducing environments in which we can manipulate and observe” (Kaijima et al. 2013). Beyond comfort and productivity, in recent months it has emerged that air flow may also be a matter of life and death. With the current global pandemic of SARS-CoV-2, it is indoor environments where infections most often happen (Qian et al. 2020). To design architecture in a post-COVID-19 environment will require an in-depth understanding of how air flows through space.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2023_234
id	sigradi2023_234
authors	Santos, Ítalo, Andrade, Max, Zanchettin, Cleber and Rolim, Adriana
year	2023
title	Machine learning applied in the evaluation of airport projects in Brazil based on BIM models
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. 875–887
summary	In a country with continental dimensions like Brazil, air transport plays a strategic role in the development of the country. In recent years, initiatives have been promoted to boost the development of air transport, among which the BIM BR strategy stands out, instituted by decree n-9.983 (2019), decree n-10.306 (2020) and more recently, the publication of the airport design manual (SAC, 2021). In this context, this work presents partial results of a doctoral research based on the Design Science Research (DSR) method for the application of Machine Learning (ML) techniques in the Artificial Intelligence (AI) subarea, aiming to support SAC airport project analysts in the phase of project evaluation. Based on a set of training and test data corresponding to airport projects, two ML algorithms were trained. Preliminary results indicate that the use of ML algorithms enables a new scenario to be explored by teams of airport design analysts in Brazil.
keywords	Airports, Artificial intelligence, BIM, Evaluation, Machine learning.
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	caadria2020_240
id	caadria2020_240
authors	Stojanovic, Djordje and Vujovic, Milica
year	2020
title	How to Share a Home - Towards Predictive Analysis for Innovative Housing Solutions
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 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 547-556
doi	https://doi.org/10.52842/conf.caadria.2020.1.547
summary	Renewed interest in cohousing solutions is driven by the rapid population growth and a lack of affordable housing in many cities across the world. The home share has become more prevalent in recent years due to the cost benefits and social gains it provides. While it involves challenges primarily concerned with the usage of communal areas, the viability of this housing model increases with the advancement of technology enabling new tools for analysis and optimisation of spatial usage. This paper introduces a method of sensor application in the occupancy analysis to provide grounding for future studies and the implementation of advanced computational methods. The study focuses on the underexplored potential of the communal spaces and provides a method for the measuring of specific aspects of their usage. The study applies principles of mathematical set theory, to give a more conclusive understanding of how communal areas are used, and therefore contributes to the improvement of housing design. Presented outcomes include an algorithmic chart and a blueprint of a behavioural model.
keywords	Cohousing; Housing share; Post Occupancy Evaluation; Machine Learning ; Predictive Analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2020_234
id	caadria2020_234
authors	Zhang, Hang and Blasetti, Ezio
year	2020
title	3D Architectural Form Style Transfer through Machine Learning
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. 659-668
doi	https://doi.org/10.52842/conf.caadria.2020.2.659
summary	In recent years, a tremendous amount of progress is being made in the field of machine learning, but it is still very hard to directly apply 3D Machine Learning on the architectural design due to the practical constraints on model resolution and training time. Based on the past several years' development of GAN (Generative Adversarial Network), also the method of spatial sequence rules, the authors mainly introduces 3D architectural form style transfer on 2 levels of scale (overall and detailed) through multiple methods of machine learning algorithms which are trained with 2 types of 2D training data set (serial stack and multi-view) at a relatively decent resolution. By exploring how styles interact and influence the original content in neural networks on the 2D level, it is possible for designers to manually control the expected output of 2D images, result in creating the new style 3D architectural model with a clear designing approach.
keywords	3D; Form Finding; Style Transfer; Machine Learning; Architectural Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaade2024_409
id	ecaade2024_409
authors	Zarzycki, Andrzej
year	2024
title	BIM-Driven Curriculum for Integrated Design Studios: Maintaining data interoperability and design flexibility
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. 27–36
doi	https://doi.org/10.52842/conf.ecaade.2024.2.027
summary	This paper presents a curricular model for an integrated design studio focused on BIM-driven processes, satisfying the NAAB 2020's student performance criteria SC.5 and SC6. These criteria emphasize quantifiable, evidence-based design thinking by requiring the provision of "measurable environmental impacts" and "measurable outcomes of building performance." The studio, serving as a capstone project, integrates accessible design, user and regulatory requirements into building assemblies, structural and environmental systems, and life safety, underscoring the importance of measurable building performance outcomes. The adoption of computational design tools, particularly Building Information Modeling (BIM), facilitates engagement in environmental and user-focused simulations and ensures data interoperability throughout the design and post-occupancy phases. Utilizing a comprehensive set of tools, including life-cycle assessment (LCA) and energy modeling, the curriculum advances beyond simple simulations to support decision-making and multi-objective optimizations. This approach enables a new form of design thinking that incorporates a broader set of variables and considerations, encouraging students to meet various environmental impact and performance benchmarks, including LEED v.5 Certification points and Architecture 2030 energy standards. The integration of scenario simulation tools empowers students to autonomously advance their projects within a framework of constraints, marking a pedagogical shift towards faculty acting as learning facilitators and promoting student autonomy in design evaluation.
keywords	building information modeling, BIM, building performance simulations, design education
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2020_306
id	caadria2020_306
authors	Akizuki, Yuta, Bernhard, Mathias, Kakooee, Reza, Kladeftira, Marirena and Dillenburger, Benjamin
year	2020
title	Generative Modelling with Design Constraints - Reinforcement Learning for Object Generation
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 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 445-454
doi	https://doi.org/10.52842/conf.caadria.2020.1.445
summary	Generative design has been explored to produce unprecedented geometries, nevertheless design constraints are, in most cases, second-graded in the computational process. In this paper, reinforcement learning is deployed in order to explore the potential of generative design satisfying design objectives. The aim is to overcome the three issues identified in the state of the art: topological inconsistency, less variations in style and unpredictability in design. The goal of this paper is to develop a machine learning framework, which works as an intellectual design interpreter capable of codifying an input geometry to form a new geometry. Experiments demonstrate that the proposed method can generate a family of tables of unique aesthetics, satisfying topological consistency under given constraints.
keywords	generative design; computational design; data-driven design; reinforcement learning; machine learning
series	CAADRIA
email
last changed	2022/06/07 07:54

For more results click below: