## 5 Conclusions
This demonstrates the model's potential to serve as a standardized tool for risk quantification, applicable across diverse project settings and adaptable to evolving project demands. This level of accuracy confirms the applicability of the methodology in real complex road infrastructure projects. By addressing key risks proactively, the modelempowers stakeholders--investors, contractors, and policymakers--to make informed decisions, ensuring better resource allocation and operational efficiency. The hybrid model significantly enhances risk management by achieving an average deviation of less than 10%, which is notably lower than traditional methods. For instance, in Project 1, the model accurately predicted design risks with only a 1.66% deviation. Moreover, its ability to rank risks based on their probabilities allowed for targeted interventions, such as prioritizing measures against external factors with a high likelihood of occurrence (20.88%). Additionally, the integration of multiple analytical tools and datasets ensures that the model is both adaptable and comprehensive, making it a valuable asset for decision making in project planning and implementation. The results demonstrate the model's ability to streamline risk management, optimize resource use, and improve outcomes. By offering a structured and quantitative framework for assessing risks, the methodology contributes to safer, more efficient, and economically sustainable road infrastructure development. Its application provides project stakeholders with the necessary insights to proactively manage risks and achieve better control over project timelines and budgets. While demonstrating the effectiveness of the developed hybrid model for risk assessment and management in road infrastructure projects, this study acknowledges several limitations. First, the accuracy and reliability of the model are inherently tied to the quality and volume of the input data. Increasing the dataset size with additional results from real-world projects would enhance the model's quality and predictive accuracy, as more diverse scenarios and risk occurrences could be accounted for in the analysis. Second, the input data used in this study were derived from projects implemented within the territory of Serbia. While the validation was conducted by a panel of experts with extensive international experience, the applicability of the model's results to other regions remains an open question. Differences in regulatory frameworks, environmental conditions, and project characteristics may affect the generalizability of the findings. Additionally, the reliance on expert evaluations introduces a degree of subjectivity into the process. Although measures were taken to ensure the consistency and reliability of expert inputs, variations in judgments or interpretations could influence the derived probabilities and conclusions. Lastly, the integration of fuzzy logic, while highly effective for managing uncertainty, assumes that the defined rules and membership functions adequately represent real-world conditions. Further refinement and validation of these components may be necessary for broader applications. Despite these constraints, this research offers a solid basis for the future development and application of hybrid risk management models, with opportunities for expansion and refinement to address these identified challenges. Future research should focus on expanding the dataset with real-world projects from diverse regions to improve the model's generalizability and quality. Incorporating additional project characteristics, such as advanced environmental factors or technological innovations, could enhance the model's complexity assessment. Developing a dynamic framework that updates probabilities in real time based on project progress and external influences would improve its practical applicability. The validation of the model across different types of infrastructure, such as railways or airports, could test its versatility.