{"title":"Data-driven generative contextual design model for building morphology in dense metropolitan areas","authors":"Ziyu Peng, Yi Zhang, Weisheng Lu, Xueqing Li","doi":"10.1016/j.autcon.2024.105820","DOIUrl":null,"url":null,"abstract":"<div><div>Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing designs. This paper aims to develop a data-driven generative design model: VmRF, which can learn from existing designs and generate plausible and contextual building morphologies. The model consists of a variational autoencoder (VAE) to compress high-dimensional building morphology datasets into low-dimensional building morphology datasets and a multivariate random forest (mRF) to identify explainable relationships between design parameters and morphology patterns. Performance evaluation shows the superiority of the VmRF model in terms of training speed and prediction fitness. Consequently, the proposed model promotes enhanced design efficiency, innovation in contextual awareness, and evidence-based decision-making in building morphology design.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005569","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing designs. This paper aims to develop a data-driven generative design model: VmRF, which can learn from existing designs and generate plausible and contextual building morphologies. The model consists of a variational autoencoder (VAE) to compress high-dimensional building morphology datasets into low-dimensional building morphology datasets and a multivariate random forest (mRF) to identify explainable relationships between design parameters and morphology patterns. Performance evaluation shows the superiority of the VmRF model in terms of training speed and prediction fitness. Consequently, the proposed model promotes enhanced design efficiency, innovation in contextual awareness, and evidence-based decision-making in building morphology design.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.