基于ConvNeXt的围岩裂隙识别提取与完整性评估方法OA

In order to overcome the strong dependence of traditional acoustic wave testing methods on testing equipment and site conditions in tunnel rock mass integrity evaluation,and to achieve rapid and convenient prediction of the rock mass integrity coefficient(Kv),an integrated rock mass integrity assessment method based on deep learning is proposed.Taking surrounding rock fracture images as input,a fracture image dataset is constructed,and a ConvNeXt-based region convolutional neural network model is introduced to realize automatic fracture identification and semantic segmentation.Subsequently,image processing techniques are employed to quantitatively analyze the identified fractures,from which seven geometric features,including fracture length,density,number of intersections,and roughness are extracted.Furthermore,a genetic programming algorithm is utilized to establish a nonlinear mapping relationship between fracture feature parameters and Kv.The results indicate that the proposed model achieves a recall rate of 91.83％in fracture identification.Cross-validation based on the genetic programming model shows that the root mean square error of Kv prediction is 0.026 3±0.009 4,and the mean absolute percentage error is 2.57％±0.46％.The proposed method effectively enables Kv prediction based on fracture image features,providing an alternative approach to rock mass integrity evaluation that does not rely on acoustic wave testing and demonstrates promising engineering application potential.