## 4 Discussion  
The machine learning models applied in this study did not yield satisfactory results in risk prediction, which can be attributed to dataset limitations. The analysis revealed that input variables did not exhibit strong linear dependencies with all output variables, with only moderate correlations observed between certain variables. Additionally, some negative correlations suggest that increasing project complexity may reduce the probability of certain risks. These findings indicate that the dataset lacks sufficient variability to fully capture complex relationships, affecting the performance of machine learning models [53; 54; 55]. One of the key findings is that the coefficient of determination (R\({}^{2}\)) remained negative across all models, even after dataset expansion. This suggests that the models struggled with generalization, which is a common issue in machine learning when dealing with small datasets and high-dimensional problems [54; 56]. The neural network model performed poorly, which was expected, as deep learning techniques typically require substantially larger training sets to avoid overfitting [57; 58]. Small datasets can limit the learning capacity of machine learning models, particularly when the relationships between variables are weak or nonlinear. In such cases, models may fail to distinguish between signal and noise, leading to unstable and unreliable predictions [53; 54; 55]. Comparing these results with findings from previous research, it is evident that datasets with fewer than a few thousand examples rarely produce strong machine learning models for predictive risk assessment [26; 27; 28; 29]. Studies that have successfully applied machine learning for risk modeling typically use larger datasets featuring engineering techniques and hybrid modeling approaches to improve accuracy [27; 28; 59]. For instance, prior research has shown that Random Forest and Gradient Boosting models can effectively capture nonlinear risk patterns, but only when the dataset is sufficiently large and diverse [28; 54]. Similarly, studies utilizing hybrid fuzzy logic and machine learning approaches have reported improved results, particularly in domains where uncertainty and limited data availability are key challenges [46; 59; 60; 61]. These insights highlight the importance of data quality and preprocessing in risk prediction models. While machine learning holds significant potential for improving risk assessment methodologies, its effectiveness largely depends on data availability, feature selection, and model optimization. On the other hand, fuzzy logic provided very promising results, particularly the Sugeno FIS. In Figures 5 and 6, comparisons between the Sugeno and Mamdani FIS structures are made. As can be noticed, from 14 considered scenarios, the Sugeno outperformed the Mamdani FIS in 12 cases. This study demonstrates the Sugeno FIS's accuracy and practicality for risk management in road infrastructure projects. The calculated probabilities of individual risks and their prioritization provide a clear foundation for targeted preventive measures. With deviations from real-world data averaging below 10%, the model ensures reliability and precision, enabling stakeholders to proactively address risks and improve project outcomes. These results highlight the model's applicability as a focused and efficient tool for advancing risk management practices in road infrastructure development. 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.