Artificial intelligence in risk management within the realm of construction projects: A bibliometric analysis and systematic literature review

Abstract

The construction industry faces risks across various domains, including cost, safety, schedule, quality, and supply chain management. Recent artificial intelligence (AI) advancements offer promising solutions to enhance risk management. This systematic literature review (SLR) explores the integration of AI in construction risk management, focusing on AI applications, risk categories, and key algorithms. A total of 84 peer-reviewed articles published between 2014 and 2024 were analysed. The SLR method involved rigorous identification, selection, and critical appraisal of studies, followed by bibliometric analysis to uncover research trends, influential authors, and thematic clusters. The bibliometric analysis, including keyword co-occurrence and author collaboration networks, provided insights into the structure of the research landscape. Findings revealed that AI methods such as machine learning (ML), natural language processing (NLP), knowledge-based reasoning (KBR), optimisation algorithm (OA), and computer vision (CV) play crucial roles in predicting and managing risks. ML is employed for predictive modelling, NLP for document and compliance risk management, KBR for decision support, OA for optimising resources and schedules, and CV for real-time safety monitoring. Despite advancements, challenges related to data quality, model interpretability, and workforce skills hinder full AI integration. Future research should explore AI’s intersection with emerging technologies such as blockchain and adaptive risk models for responsible adoption. This paper contributes to the growing knowledge of AI’s transformative impact on construction risk management.