PROBABILITY-BASED RISK IN BUILDING PROJECTS: A MULTIDIMENSIONAL SYSTEMATIC REVIEW ACROSS RISK TYPES, PROJECT STAGES, AND BUILDING USES

Abstract

Probabilistic risk management has become central to delivering safer and more resilient building projects under growing cost, schedule, and sustainability pressures. This study synthesizes how probability-based approaches have been applied in building-related risk research and identifies unresolved gaps that limit their practical impact. Using a PRISMA-guided systematic literature review of the Scopus database, we screened 165 records and qualitatively analyzed 39 Q1–Q4 journal articles published between 2015 and 2025. The review applies a multi-dimensional framework that classifies studies by risk type, project type, life-cycle stage, building type, methodology, and geographic context. Quantitative designs dominate, particularly Monte Carlo simulation and Bayesian or Dynamic Bayesian Network models, which are frequently embedded in BIM-enabled and Digital Twin environments for cost, safety, and seismic performance assessment. Evidence shows that construction and financial risks, commercial and residential buildings, and construction and operation–maintenance stages attract most scholarly attention, while pre-construction phases, infrastructure assets, and developing-country contexts remain under-explored. The review develops an integrated conceptual map that links probabilistic techniques with risk categories and project settings, and highlights the citation influence of seminal contributions. It also reveals methodological imbalances (limited mixed-methods and participatory studies) and weak integration of probabilistic risk assessment with sustainability and resilience objectives. Future research should extend probabilistic risk models to under-represented geographies and project types, exploit hybrid quantitative–qualitative and AI-enhanced approaches, and embed climate and circular-economy considerations into construction risk decision-support tools.