Fusion of finite element and machine learning methods to predict rock shear strength parameters

Abstract The trial-and-error method for calibrating rock mechanics parameters has the disadvantages of complexity, being time-consuming, and difficulty in ensuring accuracy. Harnessing the repeatability and scalability intrinsic to numerical simulation calculations and amalgamating them with the data-driven attributes of machine learning methods, this study uses the finite element analysis software RS2 to establish 252 sets of sandstone sample data. The recursive feature elimination and cross-validation method is employed for feature selection. The shear strength parameters of sandstone are predicted using machine learning models optimized by the particle swarm optimization (PSO) algorithm, including the…

• NN
  National Natural Science Foundation of China

  Awards: 52174124, 51904200
• SP
  Shanxi Provincial Key Research and Development Project

  Award: 202202090301011