## 1 Introduction
### Literature Review
The findings reveal that ANNs and Bayesian Regression are the most frequently applied and most effective methods for cost estimation, whereas hybrid models that integrate ANNs with fuzzy logic, genetic algorithms (GA), and CBR outperform standalone techniques in terms of accuracy. The study also highlights that construction and road infrastructure projects are the most common applications of these techniques. The obtained results offer valuable insights for project managers in the early project stages, providing recommendations for selecting the most suitable machine learning methods, prioritizing data processing, and integrating ML models with traditional techniques. The study by [PERSON] et al. [27] applies machine learning for delay risk analysis and prediction in construction projects, utilizing Decision Trees (DT) and Naive Bayesian Classification. The research involved identifying key risk sources, collecting multivariate datasets on previous project delays, and applying data analytics to understand the complexity and interdependencies of risk factors. The results indicated that the Bayesian model outperformed the Decision Tree model in delay prediction, confirming the assumption that conditional independence of variables is crucial in risk analysis. In another study, [PERSON] et al. [28] developed a Random Forest model to predict cost overruns in engineering services (ESCO) for construction projects. The model exhibited superior performance compared to Support Vector Regression (SVR) and Multiple Linear Regression (MLR), achieving an R\({}^{2}\) value of 0.8680 and a low mean absolute error (MAE). Artificial Neural Networks (ANNs) have also been applied in various infrastructure-related risk assessments. In [PERSON] and [PERSON]'s study [29], an ANN was used to analyze bridge deterioration, comparing its performance with k-nearest neighbors (KNNs). After hyperparameter optimization, the ANN achieved a prediction accuracy of 91.44%, enabling more precise maintenance planning and reduced inspection costs. Additionally, [PERSON] et al. [30] developed an ANN-based model for classifying economic losses in transportation infrastructure projects, employing the BFGS backpropagation algorithm, where the best model achieved a classification accuracy of 74.1% on the test dataset. Table 1 provides a summary of selected studies in the field of risk management for infrastructure projects [31], focusing on fuzzy logic, machine learning, and hybrid modeling approaches. The table highlights key focus areas, methodologies, and their relevance to this study, encompassing fuzzy logic systems, machine learning models, and comparative analyses of different computational intelligence techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Focus Area** & **Methodology/Key Findings** & **Practical Implications** & **Study** \\ \hline Risk management methodology for transport infrastructure & Developed a five-stage framework: identification, analysis, processing, recommendations, and monitoring. & Highlights the importance of structured risk management approaches. & [PERSON] et al. (2022) [PERSON] et al. In the context of road infrastructure projects [33, 34, 35, 36], where the stakes are high and the margin for error is minimal, adopting innovative risk management strategies is not just advantageous, but essential. This study builds on these advancements by proposing a novel hybrid model for risk assessment and management. The primary objective of this study is to define the probability of risk occurrence for future road infrastructure projects through the application of a hybrid model. This study analyzes the application of multiple computational intelligence techniques to a specific dataset, assessing their effectiveness in risk prediction. By comparing various computational intelligence techniques and integrating them with empirical data from real-world projects, this study identifies the most precise hybrid model, which is designed to provide a structured framework for quantifying risk probabilities.