## 3 Results
### Model for Determining the Probability of Individual Risks  
Risk assessment in road infrastructure projects requires a structured approach to quantify the probability of occurrence of individual risks. Since each risk group consists of multiple individual risks, refining probability estimation at the individual level is essential. This methodology establishes a systematic process for determining the probability of each specific risk by integrating fuzzy-logic-based outputs with empirical data from real-world projects.  
Risk Probability Model Structure  
The probability of each individual risk Ri is determined by combining multiple factors. First, the probability of the corresponding risk group RGj is obtained from the Sugeno fuzzy inference model. Next, the relative frequency of the specific risk within its group is derived from empirical project data, as presented in Table 3. Finally, adjustment factors are applied to account for project-specific uncertainties, particularly the potential impact of risks on Extension of Time (EoT) and Increasing Contract Price (ICP). This integrated approach ensures a more precise estimation of risk probabilities while considering both historical data and project-specific conditions.  
Since certain risks contribute more significantly to delays (EoT) or cost overruns (ICP), the model separately evaluates these two aspects using the following equations:  
\[P\Big{(}R^{iEoT}\Big{)}=P(RGj)\cdot P(Ri\mid RGj)\cdot(1+w_{EoT}\cdot EoT) \tag{2}\]  
\[P\Big{(}R^{iICP}\Big{)}=P(RGj)\cdot P(Ri\mid RGj)\cdot(1+w_{ICP}\cdot ICP) \tag{3}\]  
where:  
\(P(R^{iEoT})\), \(P(R^{iICP})\)--probability of the specific risk \(Ri\) in relation to \(EoT\) and \(ICP\), respectively; \(P(RGj)\)--probability of the corresponding risk group \(RGj\), determined through Sugeno fuzzy inference model;  
\(P(Ri/RGj)\)--relative frequency of individual risk \(Ri\) within its risk group \(RGj\), derived from empirical project data (Table 3);  
\(EoT\)--expected project delay factor (percentage of total project duration);  
\(ICP\)--expected cost increase factor (percentage of total project budget);  
\(w_{EOT}\), \(w_{ICP}\)--weighting coefficients reflecting the sensitivity of individual risks to time and cost impacts.  
This approach ensures that the risk probabilities are not only assessed in absolute terms, but are also adjusted based on their potential impact on project duration and cost overruns. By integrating historical risk occurrence rates (Table 3) with fuzzy model  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Risk Group**} & \multicolumn{3}{c}{**From a Real Project**} & \multicolumn{3}{c}{**From FIS F2**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-7}  & **Project 1** & **Project 2** & & & & & \\ \hline Design & 0.6113 & 0.6409 & 0.6193 & 0.5971 & 0.6053 & 0.6486 & 4.2004 \\ \hline External & 0.1151 & 0.0922 & 0.0923 & 0.1109 & 0.1084 & 0.1006 & 10.065 \\ \hline Resource & 0.1119 & 0.1056 & 0.1185 & 0.1197 & 0.1204 & 0.1104 predictions, this methodology enables project managers to systematically quantify and mitigate risks, providing a practical decision-making framework for improving control over project timelines, costs, and overall risk exposure.