ABSTRACT

In the construction industry, risk mapping represents a critical phase that determines the successful completion of a project within the planned schedule and budget. Failure to identify potential threats during the planning stage frequently leads to operational cost overruns, schedule delays, and structural or financial failure. This study focuses on the development of an expert system integrating the Fuzzy Logic method to diagnose construction project risks in a precise and adaptive manner. The system was designed to assist project managers in mitigating potential field constraints through a more measurable decision-making process. The selection of the Fuzzy Logic algorithm was based on its capability to process subjective and uncertain information commonly encountered in actual site conditions. Through this approach, risk evaluation becomes more dynamic by incorporating critical variables such as material price fluctuations, geotechnical site conditions, and the availability of skilled labor. The system architecture consists of hazard parameter identification, rule-based knowledge formulation, and fuzzy inference execution to determine risk level classifications. The risk database was developed through in-depth discussions with construction practitioners and a review of relevant literature. Validation results confirmed that the Fuzzy Logic–based expert system achieved an accuracy level of 87%, with a high-risk precision of 85.7% in predicting risk profiles and providing strategic recommendations for decision-makers. The findings indicate that the fuzzy logic approach is capable of accommodating uncertainty in field data and generating more adaptive risk classifications compared with conventional methods, thereby supporting the sustainability of construction projects across various scales. Accordingly, this expert system innovation serves as an essential digital instrument for proactive risk mitigation in project management. This study contributes to the development of fuzzy-based decision support systems for construction project risk management and provides an empirical foundation for the implementation of expert systems to improve the effectiveness of project risk mitigation.