Intelligent risk management in construction projects: Systematic literature review

Abstract

As digitalization leads to the development of the global economy and artificial intelligence stimulates technological innovation, integrating smart management methods into project management is becoming more and more critical to the development of the construction industry. As an influential branch of engineering management, intelligent risk management in the construction field will be a future research direction. Hence, the aim of this study is to find the gaps and future research trends in intelligent risk management field by Systematic Literature Review. In order to achieve these objectives, 436 articles were selected from the WOS and Scopus databases to be analyzed by CiteSpace scientometric software, and the results were classified into collaborative network analysis, co-citation analysis, and public network analysis. From the analysis, China had the highest number of publications, while the United States was the most influential country in this field. The researchers at different institutions in this field have formed research teams despite the lack of collaboration between the authors and their institutions. In co-cited references, the emphasis was on traditional methods and applications of risk analysis, with fewer citations of novel methods. According to the keywords, the clusters of keywords, and the temporal evolution of the keywords, and combining the conceptual model, 5 research gaps areas were discovered. In order to fulfill these challenges and barriers, this study found the future research trends, including: developing digital management platform for intelligent construction management and risk management, building a decision-making system for risk management to find an optimum solution, refining building digital models which would be the basis of digital management platform, identifying and category the characteristic construction risk factors by using machine learning techniques, just like text mining or knowledge graph, building an API to embed decision making system into digital management platform, and the intelligent risk management system in real-time projects to verify its possibility.