辽宁工程技术大学学报（自然科学版）2026，Vol.45Issue(3)：314-322,9.DOI:10.11956/j.issn.1008-0562.20260015
基于ConvNeXt的围岩裂隙识别提取与完整性评估方法
Fracture segmentation and rock mass integrity intelligent prediction based on ConvNeXt
摘要
Abstract
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.关键词
隧洞岩体/围岩裂隙/深度学习/图像分析/完整性系数Key words
tunnel rock mass/surrounding rock fractures/deep learning/image analysis/integrity coefficient分类
建筑与水利引用本文复制引用
张睿,张莹,栾雅琳,狄圣杰,陶莹莹,王骐恺,张艳美..基于ConvNeXt的围岩裂隙识别提取与完整性评估方法[J].辽宁工程技术大学学报（自然科学版）,2026,45(3):314-322,9.基金项目
山东省自然科学基金项目(ZR2025QC486) （ZR2025QC486）
陕西省自然科学基础研究项目(2024JC-YBQN-0357) （2024JC-YBQN-0357）
陕西省重点研发计划(2025CY-YBXM-466) （2025CY-YBXM-466）
