关键词: 隧道, 锚杆, 锚固质量评估, 数据预处理, 卷积神经网络

Abstract: The anchoring quality of bolt is usually detected by acoustic reflection method, and then analyzed and classified manually, which is not only subjective but also timeconsuming and laborious. Therefore, a bolt anchoring quality evaluation method based on Alexnet convolutional neural network is proposed. Firstly, the acoustic wave reflection signals that have been manually classified are preprocessed to obtain the original sample data set, which are divided into training set and test set in a certain proportion; and then the data are used to train the convolutional neural network model and carry out classification test. The experimental results show that: (1) The preprocessing method greatly improves the accuracy of the final classification, reaching an accuracy rate of about 90% in the sample data set. (2) In the practice engineering application, compared with the results of manually classification, the recognition degree of the classification results by the proposed method reaches 95%.

Key words: tunnel, rock bolt, bolt anchorage quality evaluation, data preprocessing, convolutional neural network