Quantifying tunnel stability using the proposed combination of back analysis and the strength reduction method (SRM) is useful during construction. To feasibly and reliably obtain geotechnical parameters for the surrounding rock (which vary in different places), a real-coded genetic algorithm is used in setting the initial parameters of the neural network to improve the prediction accuracy of the parameters via back analysis by reasonably selecting the selection operator, crossover operator, and mutation operator. After obtaining the parameters, the proposed SRM, i.e., the optimization double-strength reduction method (ODSRM), which is based on the optimization method, is used to evaluate stability. By using this method, the cohesion and friction angle have different reduction factors that are more reasonable and accurate. The combined method is verified in an application to the Yu Liao Tunnel, where it is demonstrated that the combined method can use the measured displacements to obtain the safety factor. Compared with the traditional method, the proposed back analysis method can reduce errors in the predicted performance, and unlike the SRM, the ODSRM can avoid overestimating the safety factor with the same reduction factor. Finally, the presented methods can reduce the amount of calculation required and are convenient for evaluating tunnel stability with displacement.
Determination of safety factor for rock mass surrounding tunnel by sudden change of equivalent plastic strain in strength reduction method
