Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets

Newton, David William

doi:10.5151/proceedings-ecaadesigradi2019_135

Cited by 13 publications

(7 citation statements)

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“…In architectural planning, such AI assists in developing plan layouts, different techniques described by Nauata et al (2021) and Chaillou (2019). Newton (2019) explores GANs for generating and analyzing architectural plans, even with small datasets, illustrating AI's capability to contribute to architectural plan development and analysis. Rodrigues et al (2024) apply AI to space allocation in housing, showcasing AI's role in creating efficient mass-customized layouts.…”

Section: Evaluation Of Generative Ai Toolsmentioning

confidence: 99%

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Zeytin,

Öztürk Kösenciğ,

Öner

2024

Journal of Computational Design

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This study explores the integration of Generative Design Assistants (GDAs), specifically machine learning based tools, in the architectural design process. It investigates how these tools, once confined to experimental realms, are now influencing mainstream architectural practice, particularly among novice architects. The research focuses on third and fourth-year architecture students, examining how they adapt to and integrate these advanced AI tools into their design workflows. Through an empirical online workshop, the study collected data of design process recordings, design output success scores of students by an independent jury, and post-experiment surveys. This approach provided insights into the timing, frequency, and sequence of GDA usage, as well as the influence of specific GDA features on design success. The research reveals that three primary strategies emerged in students' GDA usage: continuous use throughout the design process, selective problem-solving use, and initial ideation use followed by traditional methods. However, an over-reliance on GDAs was noted to potentially limit the designer’s interpretive and developmental input. The survey shows that different GDAs have distinct strengths and impacts on the design process. In terms of selected GDAs for the experiment, ArchiGAN aids in discovery and ideation, while HouseGAN excels in reframing design problems. In conclusion, the study underscores the transformative potential and challenges of GDAs in architectural design and highlights the need for balanced GDA integration. The research outputs show that future research should focus on the long-term implications of GDAs in architectural education. This research aims to guide the effective integration of AI in architecture, enhancing the human designer's role rather than overshadowing it.

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Section: Evaluation Of Generative Ai Toolsmentioning

confidence: 99%

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Zeytin,

Öztürk Kösenciğ,

Öner

2024

Journal of Computational Design

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“…The approach to integrate urban analysis tools with GIS can also be adopted to address other types of urban issues and problems Zhang et al (2020). New developments in urban planning require a context-specific methodology for the implementation of a 3Durban-GIS prototype Newton (2019). The findings of a study by Zhang et al (2022) support urban planning for the choice of different scenarios and alternatives of Green Infrastructure (GI) to better balance public and private costs and generate wider benefits for local communities.…”

Section: Gis In Urban Planningmentioning

confidence: 99%

UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS

Cheng,

Chu,

Xia

et al. 2023

Front. Environ. Sci.

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Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development.

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“…Thus, the data set need to be augmented. Since the learning point is the layout relation of private garden space element, as the same way researchers did it before [6], the remaining 30 samples were flipped in four directions to get a total of 120 samples for the experiment.…”

Section: Augmentationmentioning

confidence: 99%

Exploration on Machine Learning Layout Generation of Chinese Private Garden in Southern Yangtze

Liu

Fang

Yang

et al. 2021

Proceedings of the 2021 DigitalFUTURES

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Machine learning has been proved to be feasible and reasonable in architectural field by extensive researches recently, whereas its potential is far from being tapped. Previous studies show that the training of GAN by labelling can enable a computer to grasp interrelationship of spatial elements and logical relationship between spatial elements and boundary. This study set the learning object as layout of private gardens in southern Yangtze with higher complexity. Chinese scholars usually analyse private garden layout based on their observation and experience. In this paper, based on Pix2Pix model, we enable a computer to generate private garden layout plan for given site conditions by learning classic cases of traditional Chinese private gardens. Through the experiment, taking Lingering garden as example, we continuously adjust the labelling method to improve learning effect. The finally trained model can quickly generate private garden layout and aid designers to complete scheme design with private garden element corpus. In addition, the working process of training GAN enables us to discover and verify some private garden layout rules that have not been paid attention to.

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Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets

Cited by 13 publications

References 0 publications

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS

Exploration on Machine Learning Layout Generation of Chinese Private Garden in Southern Yangtze

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