Numerical modeling and neural networks to identify model parameters from piezocone tests: II. Multi‐parameter identification from piezocone data

This paper completes the study presented in the accompanying paper, and demonstrates a numerical algorithm for parameter prediction from the piezocone test (CPTU) data. This part deals with a development of neural network (NN) models which are able to map multi-variable input data onto typical geotechnical characteristics and constitutive parameters of the modified Cam clay model, which has been applied in this study. The identification procedure is designed for the coupled hydro-mechanical boundary value problem in normally-and lightly overconsolidated clayey soils including partially drained conditions that may appear during cone penetration. The NN models are trained with pseudo-experimental measurements derived with the aid of the numerical model of the piezocone test, presented in the accompanying paper. Different input configurations containing CPTU measurements and some complementary data are studied with respect to the accuracy of predicted parameter values. Finally, the performance of the developed NN predictors is tested with field CPTU data which are derived from three well-documented characterization sites, and the obtained predictions are compared with benchmark laboratory results.