## 1 Introduction
### Literature Review  
Fuzzy logic approaches have proven to be effective tools for risk assessment in complex infrastructure projects. In the study by [PERSON] et al. [19], the authors developed a risk assessment framework for replacing the pavement structure of a suspension bridge in Canada. By utilizing fuzzy logic and micro-simulation, they analyzed the effects of bridge reopening delays on the traffic network, showing that the probability of delay ranged between 18% and 40%. This model enables the more precise planning of traffic management strategies and minimizes the negative economic impacts of such delays. Similarly, [PERSON] [20] investigated the application of fuzzy logic in risk analysis for tunneling projects, where uncertainty due to unpredictable geological conditions posed a significant challenge. The study demonstrated that fuzzy logic systems provide a more flexible risk assessment compared to traditional methods, particularly when input data are imprecise or subject to variability. In the context of large-scale road infrastructure projects, the study by [PERSON] et al. [8] analyzed the application of the Sugeno fuzzy logic system for predicting Extension of Time (EoT) and Increasing Contract Price (ICP) in road infrastructure projects. On the other hand, [PERSON] and [PERSON] [21] employed a fuzzy-MATLAB model for risk analysis in highway construction, identifying 61 risk factors. [PERSON] and [PERSON] [22] compared fuzzy logic with regression analyses in risk assessment for projects in India. These studies indicate that fuzzy logic enables the more efficient identification and categorization of key risks in construction projects. Fuzzy logic has also been applied in rail infrastructure  
Figure 1: Flowchart of the hybrid risk assessment model for road infrastructure projects. projects. [PERSON] and [PERSON] [23] developed a Simulink model for risk assessment in the Moscow-Kazan high-speed rail project, allowing for the continuous monitoring and adaptation of risk management strategies throughout the project's lifecycle. In the study by [PERSON] and [PERSON] [24], fuzzy logic was applied to risk management in road construction, where the model facilitated the early identification of risk factors and their ranking based on potential budget and schedule impacts. These studies confirm that fuzzy logic-based approaches improve uncertainty assessment in infrastructure projects and enhance decision-making processes in risk management. In addition to fuzzy logic, machine learning (ML) plays a significant role in risk management. Several studies have provided comprehensive reviews of machine learning applications in this domain. One such review [25] examines the use of modern research methodologies for cost overrun risk assessment in construction projects, with a focus on fuzzy hybrid methods, such as the Fuzzy Analytical Network Process (FANP), Fuzzy Artificial Neural Networks (FANNs), Fuzzy Monte Carlo Simulations (FMCSs), and Fuzzy Bayesian Belief Networks (FBBNs). These approaches have been utilized to enhance the modeling of uncertainty and complex interdependencies among risks in high-uncertainty conditions. The literature analysis (2008-2018) indicated that fuzzy hybrid methods are effective in risk assessment, but are constrained by data subjectivity, computational complexity, and the need for continuous model updates. A systematic review conducted by [PERSON] et al. [26] explores machine learning-based cost estimation methods for construction projects from 1985 to 2020. The study analyzes various cost prediction approaches, including Artificial Neural Networks (ANNs), Fuzzy Neural Networks (Fuzzy NNs), Support Vector Machines (SVMs), Particle Swarm Optimization (PSO), Decision Trees (DTs), Bayesian Regression (RA), Case-Based Reasoning (CBR), Monte Carlo methods, and the Analytical Hierarchy Process (AHP).