{"title":"Natural language-extracted and BIM-referenced knowledge base for construction quality inspection via augmented reality","authors":"Han Liu , Donghai Liu , Junjie Chen","doi":"10.1016/j.autcon.2025.106500","DOIUrl":null,"url":null,"abstract":"<div><div>Construction quality is of upmost importance for delivering well-performed civil structures. Inspection offers a critical means to ensure construction quality. However, its effective implementation relies on an excess of domain knowledge that usually takes years to accumulate, making inspection expensive and challenging to conduct. This paper introduces a construction quality inspection knowledge base that empowers inspectors with easily accessible and intuitive construction requirement information. Natural language processing (NLP) is applied to automatically extract knowledge from construction documents such as specification and regulatory files. The extracted knowledge is linked to the building information model (BIM) using proposed association methods and semantic similarity matching. The natural language-extracted and BIM-referenced knowledge base (NLBIM-KB) is integrated into an augmented reality (AR) interface, which provides a freehand tool to assist inspectors' decision-making via on-demand construction knowledge extraction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"179 ","pages":"Article 106500"},"PeriodicalIF":11.5000,"publicationDate":"2025-08-29","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/S0926580525005400","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
Construction quality is of upmost importance for delivering well-performed civil structures. Inspection offers a critical means to ensure construction quality. However, its effective implementation relies on an excess of domain knowledge that usually takes years to accumulate, making inspection expensive and challenging to conduct. This paper introduces a construction quality inspection knowledge base that empowers inspectors with easily accessible and intuitive construction requirement information. Natural language processing (NLP) is applied to automatically extract knowledge from construction documents such as specification and regulatory files. The extracted knowledge is linked to the building information model (BIM) using proposed association methods and semantic similarity matching. The natural language-extracted and BIM-referenced knowledge base (NLBIM-KB) is integrated into an augmented reality (AR) interface, which provides a freehand tool to assist inspectors' decision-making via on-demand construction knowledge extraction.
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.