Retrieval augmented generation-driven information retrieval and question answering in construction management

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chengke Wu , Wenjun Ding , Qisen Jin , Junjie Jiang , Rui Jiang , Qinge Xiao , Longhui Liao , Xiao Li
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引用次数: 0

Abstract

Construction management is a communication-intensive field, requiring prompt responses to queries from various stakeholders to ensure project continuity. However, retrieving accurate information from project documents is hampered by the mismatch in granularity between queries and vast contents and by inherent ambiguities in information. Large language models (LLMs) and retrieval-augmented generation (RAG) offer new opportunities to address the challenges. However, their effectiveness is limited by the segmentation of documents and insufficient consideration of engineers’ preferences. Therefore, we propose a novel paradigm: RAG for Construction Management (RAG4CM). It includes three components: 1) a pipeline that parses project documents into hierarchical structures to establish a knowledge pool; 2) novel RAG search algorithms; and 3) a user preference learning mechanism. The first two components enhance granularity alignment and RAG results by integrating document-level hierarchical features with raw contents. The preference learning realizes continuously improved responses along with user-system interactions. We developed a prototype system and conducted extensive experiments, demonstrating that the knowledge pool efficiently accommodates texts, tables, and images. RAG4CM realized a 0.924 Top-3 and 0.898 answer accuracy, surpassing both open-source frameworks and commercial products. In addition, preference learning further increases answer accuracy by 1.3 % to 9.5 %. Consequently, RAG4CM enables multi-source information retrieval in a user-friendly manner, improving communication efficiency and facilitating construction management activities.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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