Аннотация
Industrial construction projects involve complex interactions among uncertainty, risk, schedule, and cost, motivating the development of advanced analytical and decision-support methodologies. This study systematically reviews methodological approaches to risk assessment and performance management in industrial construction. Following the PRISMA 2020 guidelines, searches of Scopus and Web of Science identified 234 records, of which 56 articles published in Q1-ranked journals between 2011 and 2025 met the pre-specified eligibility criteria. The selected studies were classified according to methodological family, uncertainty representation, and the degree of time–cost–risk integration. Four dominant methodological families were identified: multicriteria and fuzzy decision-making, probabilistic and simulation-based modeling, optimization-based planning and resource allocation, and data-driven and artificial intelligence methods. Twenty-seven studies assessed risk independently of time and cost, whereas only four jointly modeled all three dimensions. Probabilistic and optimization-based methods demonstrated the highest level of integrated analysis, while most machine-learning approaches remained prediction-oriented and most existing models were static rather than adaptive. Based on this synthesis, the review proposes a unified analytical taxonomy and a research agenda for integrated decision-support frameworks that combine dynamic uncertainty updating, predictive analytics, and multi-objective optimization. The findings identify methodological gaps and provide a foundation for adaptive models supporting robust decision-making in complex industrial construction environments.