Modelling of particle flow code geotechnical material parameter relationships based on orthogonal design and back propagation neural network

The utilisation of particle flow code to establish discrete element models represents an effective approach for addressing the issue of discontinuous media. This methodology has been employed by numerous scholars to analyse the mechanical properties and damage laws of geotechnical materials. However, the complex nature of the particle action mechanism within the discrete element model necessitates a considerably longer time frame for the completion of an elaborate simulation experiment than that required for a laboratory test. This presents a significant challenge for researchers seeking to investigate the mechanical properties of a large number of geotechnical materials through the discrete element method. In order to accelerate the prediction of mechanical properties for various specific discrete element models, a mathematical model of the geotechnical micro-parameters and the geotechnical strength macro-parameters has been developed using an orthogonal design considering interactions and a back propagation neural network based on Bayesian regularisation. The geotechnical strength macro-parameters, such as compressive strength and tensile strength, can be derived directly from the geotechnical micro-parameters of the discrete element models through this mathematical model. The results show that the trained network model demonstrates an aptitude for predicting the uniaxial compressive strength, tensile strength, cohesion, and friction angle of geotechnical materials. The mean square error is 11.611 for the training set and 14.207 for the test set. In the test set, the median deviation rates of the predicted values of the four strength macro-parameters from the target values are 3.90%, 4.82%, 4.30%, and 7.30%.