Xinwei Zhuang , Pinru Zhu , Allen Yang , Luisa Caldas
{"title":"Machine learning for generative architectural design: Advancements, opportunities, and challenges","authors":"Xinwei Zhuang , Pinru Zhu , Allen Yang , Luisa Caldas","doi":"10.1016/j.autcon.2025.106129","DOIUrl":null,"url":null,"abstract":"<div><div>Generative design has its roots in the 1990s and has become an intense research topic for bringing the power of artificial intelligence to various aspects of architecture practices. The recent advancements in artificial intelligence have made a methodological shift in innovative approaches to generative design, fueled by the proliferation of big data. This paper provides a comprehensive review of emerging machine learning algorithms and their applications in architecture. It investigates the concepts and principles behind machine learning, assesses the strengths and limitations of current algorithms, and examines their applications and exploratory uses with a data-centric approach. This work aims to assess current research, identify emerging opportunities and challenges, and suggest viable solutions for future investigations. This work contributes to a deeper understanding of the rapidly evolving landscape of machine learning in architecture, shedding light on how the field can adapt to and leverage these transformative changes.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106129"},"PeriodicalIF":9.6000,"publicationDate":"2025-03-26","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/S0926580525001694","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 its roots in the 1990s and has become an intense research topic for bringing the power of artificial intelligence to various aspects of architecture practices. The recent advancements in artificial intelligence have made a methodological shift in innovative approaches to generative design, fueled by the proliferation of big data. This paper provides a comprehensive review of emerging machine learning algorithms and their applications in architecture. It investigates the concepts and principles behind machine learning, assesses the strengths and limitations of current algorithms, and examines their applications and exploratory uses with a data-centric approach. This work aims to assess current research, identify emerging opportunities and challenges, and suggest viable solutions for future investigations. This work contributes to a deeper understanding of the rapidly evolving landscape of machine learning in architecture, shedding light on how the field can adapt to and leverage these transformative changes.
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.