Determination of granite deformation and failure stages using the long short term memory neural network

Determination of deformation and failure stages is a fundamental issue in analyzing the movement processes of a rock. Due to the data distribution with an isochronous interval of the laboratory test video image, the long short term memory neural network (LSTM-NN) may be used to determine the deformation and failure stages of the rock under the external load. In this study, the stress-strain curve and the fissure distributions in the test video images photographed during the laboratory uniaxial compression tests of the granite specimen are used, and the deformation and failure stages of the rock are divided into compression deformation, elasticity deformation, fissure propagation, and complete failure stages. After extracting the main digital features (area) corresponding to these stages, a classification network for dividing the deformation and failure stages of the rock is established based on the LSTM-NN model. The influences of the main parameters (including learning ratio and maximum epoch) in the model on the classification precision are also examined. The determination of deformation and failure stages are furthermore performed using the model. The results shows that among the parameters of the LSTM-NN model, the learning ratio and the maximum epoch have a relatively great influence on the determination precision for the deformation and failure stages with the maximum precision if 0.005 and 200 are set respectively for these two parameters. As for the whole deformation and failure stages, the LSTM-NN model has the best and worst precisions respectively to determinate the fissure propagation and complete failure stages. As for the various compositions included in the rock, the great-to-small order of the determination precision for the deformation and failure stages is fissure, biotite, feldspar, and quartz.