The Data-Driven Homogenization of Mohr–Coulomb Parameters Based on a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN)

Abstract

Homogenization methods can characterize the mechanical properties of these materials based on appropriate constitutive models and data. They are also applied to the characterization of mechanical parameters under complex geotechnical conditions in geotechnical engineering because of the complexity and heterogeneous nature of geotechnical materials. Unfortunately, existing homogenization methods for geotechnical mechanical parameters often incur immense computational costs. Hence, a framework that utilizes finite element analysis for generating a dataset which is then trained using a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN) to obtain the homogenized Mohr–Coulomb parameters of the soils is proposed. This is the first time that Bayesian optimization and a BP-ANN have been used in conjunction to predict the homogenized mechanical parameters of soils. The dataset used for training the data is generated using the commercial FEM software ABAQUS (6.10). The maximum difference between the top and bottom part of the tunnel of the heterogeneous model and homogeneous model of our test cases only varies by 5.3%, thereby verifying the excellence of the Bayesian Optimized BP-ANN.