Rock mass quality classification based on deep learning: A feasibility study for stacked autoencoders

Abstract

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable stability assessment. To develop a tool that can deliver quick and accurate evaluation of rock mass quality, a deep learning approach is developed, which uses stacked autoencoders (SAEs) with several autoencoders and a softmax net layer. Ten rock parameters of rock mass rating (RMR) system are calibrated in this model. The model is trained using 75% of the total database for training sample data. The SAEs trained model achieves a nearly 100% prediction accuracy. For comparison, other different models are also trained with the same dataset, using artificial neural network (ANN) and radial basis function (RBF). The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5% and 98.7%, repectively, which are calculated from the confusion matrix. Moreover, this model is further employed to predict the slope risk level of an abandoned quarry. The proposed approach using SAEs, or deep learning in general, is more objective and more accurate and requires less human intervention. The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.