Determination of the shear failure areas of rock joints using a laser scanning technique and artificial intelligence algorithms

Abstract

Two artificial intelligence models-backpropagation neural network (BPNN) and support vector machines (SVM)-were created to investigate the effects of mesostructure characteristics on the shear mechanical behaviors of rock joints. This was achieved through learning training samples for the evaluation of the five basic geometrical parameters sensitivity (i.e., slope angle, horizontal orientation, elevation difference, curvature, and aperture distribution), and for determination of the shear failure regions when rock joints were subjected to low-normal and shear loads. First, the digital elevation models (DEMs) of rock joints were produced through point clouds collected using a laser scanning system. Five geometrical parameters were specified as the inputs for the artificial intelligence models, and an approach was developed to calculate the 3D aperture distribution using a point cloud registration algorithm. Shear failure regions were considered as the outputs, which were extracted from images taken after direct shear testing via the global threshold algorithm. Secondly, BPNN and SVM models were employed in order to establish relationships between the geometrical parameters and shear failure areas by machine learning on training samples. Thirdly, the information value (IV) algorithm was used to verify the two trained models. Results showed that the BPNN and SVM models made acceptable determinations of the shear failure areas, which corresponded to the real situation. The predictions from the BPNN and SVM models were more accurate than those from the IV algorithm. Furthermore, shear failure regions depended primarily on the aperture distribution of rock joints during the shearing process, followed by horizontal orientation, elevation difference, and then the slope angle. The effect of parameter curvature on shear failure was less than the other four parameters.