Understanding Artificial Neural Networks as a Transformative Approach to Construction Risk Management: A Systematic Literature Review

The construction industry is characterized by complexity and high risk, making effective risk management essential for project success. Traditional risk management methods, which often rely on expert judgment and historical data, are increasingly inadequate for addressing modern construction projects’ dynamic and multifaceted challenges. This study systematically reviews applications of artificial neural networks (ANNs) in construction risk management, covering studies published between 1990 and 2024. Following PRISMA 2020 guidelines, an initial TITLE-ABSTRACT-KEYWORD search in Scopus (1990–2024) yielded 4648 records. After applying subject area and publication-type filters, 2483 records remained. Following duplicate removal, title and abstract screening reduced the pool to 132. After a full-text eligibility assessment, 86 studies were retained. Two additional studies were identified through co-citation analysis, and after the exclusion of four retracted papers, 84 studies were included in the final synthesis. Relevant peer-reviewed studies were categorized to evaluate ANN models, their applications, and key findings. The results indicate that ANNs, including backpropagation and radial basis function networks, have been applied effectively in cost estimation, schedule prediction, safety assessment, and quality control tasks. They offer advantages compared with conventional approaches, such as improved pattern recognition, faster data processing, and more accurate risk evaluation. At the same time, critical challenges persist, including data quality, computational demands, and the interpretability of outputs. To address these issues, studies increasingly recommend integrating ANNs with hybrid approaches such as fuzzy logic, genetic algorithms, and Monte Carlo simulations, as well as leveraging real-time data through IoT and BIM frameworks. This review contributes to theory and practice by consolidating fragmented evidence, distinguishing theoretical and practical contributions, and offering practical recommendations for industry adoption. It also highlights future research directions, particularly the integration of hybrid models, explainable AI, and real-time data environments.