## 5 Conclusions  
This study presents the development and application of a hybrid model for risk assessment and management in road infrastructure projects. The model successfully integrates project-specific characteristics, expert evaluations, empirical data, and probabilistic methods to quantify the probabilities of risk occurrence. By systematically addressing major and secondary risks, the model enables project managers to identify critical risks and implement targeted preventive measures, thereby reducing potential delays and cost overruns. This study offers a practical framework that combines predictive accuracy with adaptability, addressing common challenges in road infrastructure projects and enhancing decision-making processes. The application of machine learning in this study did not yield satisfactory results in predicting risk probabilities, primarily due to the small dataset size and the specific nature of the available data. Machine learning models, such as Random Forest, Gradient Boosting, and neural networks, typically require large and diverse datasets to learn complex patterns and relationships between variables effectively. In this study, despite dataset expansion, the coefficient of determination (R2) remained negative across all models, indicating that the models struggled with generalization and predictive accuracy. This limitation can be attributed to the high-dimensional nature of the problem, the limited variability in input features, and the lack of sufficient training examples, all of which hinder the ability of machine learning models to extract meaningful patterns. The neural network model, in particular, performed poorly, as expected, since deep learning methods require substantial amounts of data to avoid overfitting and to generalize effectively. These findings align with previous research indicating that machine learning models are highly sensitive to data availability and often fail to provide strong predictions when trained on small datasets. In contrast, the application of fuzzy logic systems, particularly the Sugeno FIS, proved to be far more effective for risk assessment in road infrastructure projects. Fuzzy logic methods excel in scenarios where data availability is limited and where uncertainty and imprecision in expert assessments need to be systematically incorporated into the decision-making process. The comparison of Sugeno and Mamdani fuzzy inference systems demonstrated that Sugeno FIS consistently outperformed Mamdani FIS in 12 out of 14 tested scenarios, confirming its higher accuracy, stability, and predictive reliability. This suggests that fuzzy logic is better suited for modeling risk probabilities in cases where traditional machine learning struggles due to data constraints. By leveraging rule-based reasoning and expert-defined membership functions, Sugeno FIS was able to model uncertainty more effectively, producing risk assessments that closely aligned with real-world observations. These results underscore the importance of selecting appropriate modeling techniques based on data characteristics. While machine learning approaches remain promising for large-scale risk prediction problems, their applicability in small-sample scenarios with complex interdependencies remains limited. Conversely, fuzzy logic systems, particularly Sugeno FIS, have demonstrated strong adaptability, making them a more suitable choice for risk assessment in infrastructure projects with constrained data availability. Future research should focus on expanding the dataset, improving feature selection methodologies, and integrating hybrid approaches that combine fuzzy logic with machine learning techniques to leverage the strengths of both paradigms for enhanced predictive accuracy. Testing conducted on real-world road infrastructure projects demonstrated the Sugeno FIS model's high reliability and accuracy, with average deviations from observed probabilities being less than 10%.