Analysis of the factors influencing the nonuniform deformation and a deformation prediction model of soft rock tunnels by data mining

Due to the influence of the rock mass structure, ground stress, groundwater conditions and construction process, the distribution of the strength and stress of surrounding rock in the soft rock tunnel is nonuniform. The supporting structure may undergo nonuniform deformation and local damage, which has a considerable impact on the safe construction of the tunnel. In this paper, two reference indexes, basic deformation grade and deformation nonuniformity grade, are defined to classify the basic deformation and deformation nonuniformity of an excavation section. The influencing factors of the nonuniform deformation are reduced using the Fuzzy Delphi-Rough Set and then used as the input parameters of a back-propagation neural network (BPNN). Taking the average relative deformation and deformation nonuniformity coefficient as the output parameters, the BPNN model for the nonuniform deformation of the soft rock tunnel is established and verified by actual engineering data. In this study, the influencing factor weights of the nonuniform deformation of the soft rock tunnel are quantified by combining the subjective and objective weight calculation methods. The prediction results of the BPNN after the factor reduction are consistent with the actual results. According to the prediction grade of the basic deformation and deformation nonuniformity, the excavation method and basic support strength, and the abnormal deformation support strength of the tunnel can be optimized, respectively; this approach can provide targeted guidance for planning the safe construction of soft rock tunnels.