# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 3 Risk Assessment Methodology
### New Risk Assessment Model  
Based on field management experience and the risk identification coupling matrix, a risk assessment index system for drilling and blasting construction is established. As shown in Figure 4, the weights and membership degrees are analyzed, and a nonlinear operator is introduced to develop a combined weighting-nonlinear FAHP risk assessment model for drilling and blasting construction. The specific evaluation process is as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Definition** \\ \hline
1.5 & The influence of the indicator factor is minimal and hardly significant. \\
2.5 & The indicator factor has a modest but discernible influence. \\
3.5 & The impact of the indicator factor is clearly noticeable and somewhat significant. \\
4.5 & The indicator factor has a substantial and highly noticeable influence. \\
5.5 & The impact of the indicator factor is overwhelmingly significant and dominant. \\
2.0, 3.0, 4.0, 5.0 & The median between adjacent scales, indicating the scale at times when it falls \\  & somewhere in between two adjacent scales \\ \hline \hline \end{tabular}
\end{table}
Table 4: Criteria for prominent influence coefficient.  
For the weight part, compare the importance of each risk indicator in the risk factor set using the 1-9 scale method, obtain the corresponding judgment matrix, and perform consistency testing using the consistency ratio (CR). If the consistency test is not satisfied, reconstruct the judgment matrix for adjustment. Solve the judgment matrix that satisfies the consistency test to obtain the maximum eigenvalue and corresponding eigenvector, normalize the eigenvector to obtain the subjective weights and indicator layer weights. Use PCA to extract principal components from the expert scoring data and further calculate the objective weights of each indicator. Use the game theory combined weighting method to coordinate and allocate subjective and objective weights, then combine the subjective and objective weights to obtain the combined weights of the factor layer.
2. For the membership degree part, construct the corresponding risk assessment set and risk factor set, use the expert evaluation method to evaluate the indicators of the risk factor set to obtain the membership degree vector, and then convert it into a fuzzy relation matrix.
3. Substitute the combined weights of the secondary indicators and the fuzzy relation matrix into the nonlinear fuzzy comprehensive evaluation to obtain the evaluation result vector and convert it into a fuzzy evaluation matrix. Combine the indicator layer weights for secondary nonlinear fuzzy comprehensive evaluation to obtain the secondary nonlinear fuzzy comprehensive evaluation result vector, and determine the risk level based on the membership degree. The evaluation process is shown in Figure 4.
4. Compared with existing evaluation systems, the new model improves the risk identification method and the weight part that significantly affects the evaluation results. The weights are divided into subjective and objective parts, and the game theory combined weighting method is used to integrate the subjective and objective weights, combining the advantages of both weighting methods, reducing subjectivity, and obtaining a combined weight that is more consistent with the actual situation. At the same time, a nonlinear operator is introduced for comprehensive calculation, making the evaluation results more reasonable.  
Figure 4: Geological section of Daliangshan No. 1 tunnel.