Sensitivity analysis on a multilayer perceptron model for recognizing liquefaction cases

In this paper, a new approach is presented for quantifying the system sensitivity of key parameters influencing the recognition of field liquefaction cases in a multilayer perceptron neural network (MLP model). A novel index, the average sensitivity factor, SFi, derived from the mathematical formulation of neural network is proposed to quantify the result of the sensitivity analysis. The SFi is a robust index of sensitivity analysis for the MLP model and can be used in the other problems not just in the recognition of field liquefaction problem. A well-trained MLP model is first developed to discriminate between the cases of liquefaction and non-liquefaction. Excellent performance and good generalization is achieved, with the higher recognition rate 98.9% in the training phase, 91.2% in testing phase and 96.6% on the overall cases. Using this model, the SFi values are then calculated and reveal that peak ground acceleration (PGA) is the most sensitive factor in both the liquefaction and non-liquefaction cases. Earthquake parameters (Mw and PGA), the stress state parameters of the soil layer (rd, σV   and σv′), and the soil resistance parameters (SPT-N, CN, CE and FC) play approximately equal roles. The seismic demand factors (Mw, PGA, rd, σV  , and σv′) is more sensitive than the liquefaction resistance capacity factors (SPT-N, CN, CE, and FC) in the two-class liquefaction recognition problem.