Risk Assessment Models in Engineering Projects Using Applied Scientific Techniques

Abstract

Risk assessment is a critical scientific activity in engineering projects, aimed at identifying, analyzing, and mitigating uncertainties that threaten project performance. With increasing project complexity, traditional qualitative approaches alone are insufficient. This study presents a comprehensive analysis of risk assessment models used in engineering projects, emphasizing applied scientific techniques such as probabilistic modeling, fuzzy logic, Bayesian networks, Monte Carlo simulation, and artificial intelligence-based approaches. Comparative tables and conceptual charts are used to evaluate model applicability, strengths, and limitations. The findings indicate that hybrid and data-driven models significantly improve predictive accuracy and decision-making effectiveness in complex engineering environments.