How knowledge is represented in AEC industry: a systematic review

Purpose - This paper aims to provide insightful guidance for researchers in the architecture, engineering and construction (AEC) industry by exploring the rapid advancements in knowledge representation (KR) technology. The study identifies key trends, technologies, and applications to facilitate innovation and informed decision-making in the field. Design/methodology/approach - The research adopts a systematic review methodology, analyzing literature on KR from the past 17 years. It combines bibliometric analysis using CiteSpace with a critical examination of KR applications within the AEC industry. A KR-based framework is developed, highlighting key technologies and predominant use cases across the project lifecycle. Findings - The review highlights the diverse applications of KR across various project phases and proposes a structured framework to guide future research. In addition, the study reveals that integrating deep learning enhances data analysis and automation, while large language models (LLMs) offer significant potential for intelligent decision-making and innovation in the AEC industry. Originality/value - This paper provides a comprehensive synthesis of KR technologies and their applications specifically within the AEC industry, offering a unique framework and highlighting emerging opportunities (e.g. the use of LLMs) for researchers and practitioners who aim to drive innovation in the industry.