{"title":"AI model for analyzing construction litigation precedents to support decision-making","authors":"Wonkyoung Seo , Youngcheol Kang","doi":"10.1016/j.autcon.2024.105824","DOIUrl":null,"url":null,"abstract":"<div><div>Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization and winner prediction. By automatically summarizing the data and predicting litigation outcomes, the proposed model aids practitioners in making timely decisions and enhances document management during disputes. This paper contributes to existing knowledge in two ways. Firstly, the model aids practitioners in making timely decisions about proceeding with litigation. Secondly, unlike previous studies that manually processed raw data such as contracts and specifications, this study utilized NLP to process raw litigation case data automatically. As big data becomes increasingly common, the methodology employed in this study holds academic significance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-10-10","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/S0926580524005600","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
Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization and winner prediction. By automatically summarizing the data and predicting litigation outcomes, the proposed model aids practitioners in making timely decisions and enhances document management during disputes. This paper contributes to existing knowledge in two ways. Firstly, the model aids practitioners in making timely decisions about proceeding with litigation. Secondly, unlike previous studies that manually processed raw data such as contracts and specifications, this study utilized NLP to process raw litigation case data automatically. As big data becomes increasingly common, the methodology employed in this study holds academic significance.
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.