Risk Prediction in Road Infrastructure Projects Considering Project Complexity Coefficients

Risks causing delays in the construction of roads and highways frequently lead to substantial economic and social consequences, with project timelines extending up to three times beyond their initial schedules. These risks not only extend the project timeline but also escalate the overall project execution cost. Despite extensive research on construction-related risks globally, a notable gap remains in studies specifically addressing the risk factors that cause delays in road projects. Analysing completed projects is crucial to derive practical and applicable results, as they offer essential insights into the real-world challenges and risks that may cause timeline extensions and budget escalations. Such an approach ensures that the findings are grounded in actual project outcomes, thereby enhancing their relevance and effectiveness in improving future project planning and risk management. For these reasons, this study aims to analyse 25 project characteristics across 28 completed projects, from which three project complexity coefficients are derived. Additionally, a list of risks is defined based on expert evaluations, and the dominant risks are identified. For each of these dominant risks, a prediction model is constructed using Sugeno fuzzy logic, enabling more accurate and sustainable risk management and mitigation in future projects.