Year: 2026
Type: article
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.
Author: Ravindra Patil
Institution: Government of Maharashtra
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7216648977