Method for surrounding rock mass classification of highway tunnels based on deep learning technology

By extracting the relevant information of surrounding rock classification of road tunnel face using the deep learning technology, a multilayer convolution neural network model is established to recognize the distributive features of surrounding rock including joints, cracks, broken situations, rough degrees, smooth degrees, mud stone and water burst, etc. The deep learning AlexNet model is modified to count the number and spacing of rock joints. The deep convolution is used to extract different rock boundaries, and the specific species of rock are determined by the comprehensive color model. The degree of development of structural plane, rock hardness, structural plane roughness, groundwater development, structural types and degree of grade factors of the surrounding rock classification are qualitatively described for the results of the surrounding rock classification so as to obtain the final results of the surrounding rock classification. The results show that the deep learning model is applicable to identify different morphological characteristics of the surrounding rock. Based on the Matlab interface technology, image recognition technology, boundary extraction technology and HIS color model, the comprehensive judgement of surrounding rock classification of highway tunnels is realized. In order to verify its feasibility and accuracy, the classification results of the deep learning technology are compared with those of the traditional BQ classification.