Back Analysis of Tunnel Response Using the Differential Evolution Genetic Algorithm (DEGA)

Computational models are powerful tools in the performance-based engineering analysis and design of geotechnical structures. However, the key problem in using computational models is the paucity of reliable information needed to generate the input data. Using back analysis, field monitoring can provide the means to verify predicted response, and to check the validity of values of input parameters used in the model. The paper investigates the potential use of the Differential Evolution Genetic Algorithm (DEGA) in the back-analysis of tunnel response in order to obtain improved estimates of model parameters by matching model prediction with monitored response. DEGA is implemented in the finite difference code FLAC using FLAC's built-in language FISH. The performance of DEGA in back-analyzing tunnel response is demonstrated using a real case involving a section of the Heshang Road Tunnel in China. The use of DEGA in back-analysis of tunnel response is analyzed in terms of the stability and efficiency of the procedure under highly non-linear problems, the sensitivity of the solution to the initial trial assumption and the sensitivity of the solution to the monitoring data. It is shown that DEGA exhibits excellent convergence and repeatability characteristics, and that the convergence is independent of the initial values of the parameters. The paper recommends DEGA as a very viable technique in the back analysis and computational modeling of tunnel response.