Advantages and Limitations of Bayesian Approaches to Decision-Making in Construction Management: A Critical Review (1988–2023)

The construction industry has undergone a significant transformation with the advent of Industrial Digitalization 4.0, generating massive amounts of data across all phases of construction projects. This data explosion presents both opportunities and challenges for construction management in terms of effectively managing information and extracting valuable knowledge to support decision-making. In response to this challenge, the Bayesian approach has emerged as a powerful framework for addressing the complexities and uncertainties inherent in construction projects. This study provides a comprehensive overview of the development and applications of Bayesian approaches in construction management. Based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review methodology and supported by objective analytics techniques and digital tools, the study delineates the conceptual and methodological evolution from 1988 to 2023 of four primary Bayesian approaches: estimation-inference, modeling, simulation, and classification. The review synthesizes the advantages, limitations, and challenges associated with these approaches, highlighting their potential to incorporate prior knowledge, update beliefs based on new evidence, and model complex relationships among variables. The findings reveal a mature and diverse research landscape, with Bayesian methods being leveraged to improve decision-making across various stages and aspects of construction projects. As the construction industry continues to embrace digital transformation and data-driven approaches, the integration of Bayesian methods with emerging technologies and practices is likely to open up new opportunities for enhanced decision support and project success. This study provides valuable insights for researchers and practitioners seeking to leverage the power of Bayesian approaches in construction management.