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Automatic Identification of Rock Structure at Tunnel Working Face Based on Deep Learning
(1 Southwest Transportation Construction Group Co., Ltd., Kunming 650031; 2 Key Laboratory of Geotechnical and Subsurface Engineering of the Ministry of Education, Tongji University, Shanghai 200092)
Abstract The rapid and accurate acquisition of the apparent rock structure characteristics of tunnel working faces during the construction stage from images of the tunnel face is of great significance for the understanding of the stability of the surrounding rocks to be excavated and for the decision-making during the follow-up construction stage.This paper uses self-developed digital photographic equipment to acquire 42,100 image samples from more than 150 working faces of 13 tunnels on the Mengzi-Pingbian Highway in Yunnan under different working conditions,temperatures and humidity, illuminations and dust concentrations, selects 5 main structural categories, such as blocky, layered, fractured, granular and mosaic structures, that appear in the field data set, and develops the Tensorflow-GPU-based convolutional neural network Inception-ResNet-v2 model for tunnel face rock structures with the loss rate, precision rate and recall rate of training and testing as the main evaluation indicators. Through model training it achieves automatic identification and classification of rock structure categories. The study shows that: (1) using the tunnel face images in the training and testing sets to classify the rock categories in the model, it could achieve a precision of 98.21% and 94.61% in the training and testing sets respectively, and a recall rate of 96.14%;(2) the visualization results of the testing show that the present framework has better robustness for complex site conditions, while the phenomenon of partial identification errors should be circumvented by further improving sample richness and texture diversity.
Abstract：
The rapid and accurate acquisition of the apparent rock structure characteristics of tunnel working faces during the construction stage from images of the tunnel face is of great significance for the understanding of the stability of the surrounding rocks to be excavated and for the decision-making during the follow-up construction stage.This paper uses self-developed digital photographic equipment to acquire 42,100 image samples from more than 150 working faces of 13 tunnels on the Mengzi-Pingbian Highway in Yunnan under different working conditions,temperatures and humidity, illuminations and dust concentrations, selects 5 main structural categories, such as blocky, layered, fractured, granular and mosaic structures, that appear in the field data set, and develops the Tensorflow-GPU-based convolutional neural network Inception-ResNet-v2 model for tunnel face rock structures with the loss rate, precision rate and recall rate of training and testing as the main evaluation indicators. Through model training it achieves automatic identification and classification of rock structure categories. The study shows that: (1) using the tunnel face images in the training and testing sets to classify the rock categories in the model, it could achieve a precision of 98.21% and 94.61% in the training and testing sets respectively, and a recall rate of 96.14%;(2) the visualization results of the testing show that the present framework has better robustness for complex site conditions, while the phenomenon of partial identification errors should be circumvented by further improving sample richness and texture diversity.
QIN Shangyou1 CHEN Jiayao2 ZHANG Dongming2 YANG Tongjun1 HUANG Hongwei2 ZHAO Shuai2
.Automatic Identification of Rock Structure at Tunnel Working Face Based on Deep Learning[J] MODERN TUNNELLING TECHNOLOGY, 2021,V58(4): 29-36
2021, Vol. 58 