{"title":"Generative AI in architectural design: Application, data, and evaluation methods","authors":"Suhyung Jang, Hyunsung Roh, Ghang Lee","doi":"10.1016/j.autcon.2025.106174","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a systematic review of generative artificial intelligence (AI) use in architectural design from 2014 to 2024, focusing on 1) AI models and theory-application gaps, 2) design phases, tasks, and objectives, 3) data types and contents, and 4) evaluation methods. Based on 161 journal papers selected using preferred reporting items for systematic reviews and meta-analysis (PRISMA), the analysis reveals the theory-application gap has been reduced by 96.09 %, from 62 to 2.5 years, highlighting rapid AI adoption since 2021 with generative adversarial networks (GANs) leading, and transformers and diffusion models gaining traction. For its application, AI is employed in schematic design phases in 68.94 %, while later phases remain underexplored. Regarding types of data used, images dominate at both input (52.8 %) and output (68.32 %), with multimodal and graph data showing promise. For evaluation, comparative evaluation was most utilized (60.9 %) supported by subjective assessment by authors (34.2 %) and third parties (17.4 %).</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106174"},"PeriodicalIF":9.6000,"publicationDate":"2025-04-03","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/S0926580525002146","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
This paper presents a systematic review of generative artificial intelligence (AI) use in architectural design from 2014 to 2024, focusing on 1) AI models and theory-application gaps, 2) design phases, tasks, and objectives, 3) data types and contents, and 4) evaluation methods. Based on 161 journal papers selected using preferred reporting items for systematic reviews and meta-analysis (PRISMA), the analysis reveals the theory-application gap has been reduced by 96.09 %, from 62 to 2.5 years, highlighting rapid AI adoption since 2021 with generative adversarial networks (GANs) leading, and transformers and diffusion models gaining traction. For its application, AI is employed in schematic design phases in 68.94 %, while later phases remain underexplored. Regarding types of data used, images dominate at both input (52.8 %) and output (68.32 %), with multimodal and graph data showing promise. For evaluation, comparative evaluation was most utilized (60.9 %) supported by subjective assessment by authors (34.2 %) and third parties (17.4 %).
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.