Research purposes:The fracture zone is one of the most common geological anomalies in tunnel construction,which is prone to gushing water and mud,landslides and other geological disasters.To help solve the problems of relying on experience and low accuracy of traditional geological prediction interpretation,an intelligent identification algorithm of tunnel fault fracture zone based on Convolutional Neural Network(CNN)was proposed to assist professionals in identifying fault fracture zone quickly and effectively.Research conclusions:(1)Based on the geological background and standardized forecast picture data set,the YOLOv5 deep learning framework was adopted,the BoTNet module was introduced,and the self-attention mechanism was combined to form an intelligent identification algorithm for fault fracture zone(New YOLOv5),which can realize the intelligent identification of its location,scale and other elements.(2)Compared with the traditional YOLOv5 algorithm,the optimized algorithm has a higher accuracy in identifying undesirable geologic bodies,with the growth rates of mAP as well as mAPmax values of 13.68%and 9.96%,respectively,where the highest mAP value can reach 84.79%.(3)To a certain extent,the research results can promote the further improvement of the interpretation level of the results of the over-advance prediction of tunnel fault fracture zones,and realize the technological progress from"experience-based,varying quality"to"intelligent identification,fast and effective",which can provide favorable technical support for the intelligent construction of tunnels,and has a good prospect of engineering application.
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