摘要
为实现岩体裂隙自动化智能提取与参数化表征,提出一种基于岩体图像的裂隙智能识别与定量分析方法.首先,改进D-LinkNet网络模型框架,级联空洞卷积(CAC)融合剩余多核池化(RMP)模块建立融合多尺度特征的MD-LinkNet(Multiple-scale-feature,D-LinkNet)模型,实现裂隙多尺度特征信息的有效提取;其次,利用DUpsample双线性插值进行上采样,优化卷积池化后的裂隙低分辨率图像,通过像素邻域信息融合裂隙全局特征与局部特征,提高裂隙识别精度;再次,基于裂隙智能识别结构图构建二维裂隙特征点集,提出了交点识别和裂隙骨架矢量化计算方法,提取了裂隙的迹长、宽度、视倾角等裂隙形态参数,实现了裂隙识别图像的定量化分析.最后,依托新疆某工程开展了裂隙智能识别方法的应用分析,结果表明,该方法用于裂隙识别其准确率高达95.7%,精确率为85.1%,实现了隧道岩体的几何参数自动提取.该研究为后续地下工程岩体裂隙快速智能识别与几何参数提取提供了参考.
An intelligent identification and quantitative analysis method of rock fracture is proposed based on images,in order to realize the automated extraction and parametric characterization of fractures.Firstly,the D-LinkNet model framework was improved.The MD-LinkNet model is established by cascading cavity convolution(CAC)fusion with residual multicore pooling(RMP)module,which realized the effective extraction of multi-scale fracture feature information.Secondly,DUpsample bilinear interpolation function is utilized for up-sampling to optimize the low-resolution image of the fracture after convolutional pooling.The global and local features of the fracture are fused through the neighborhood pixel information to improve the accuracy of identification.Further,a two-dimensional fracture feature set is established based on the identification maps.A method of intersection identification and skeleton vectorization calculation for fractures is proposed,which extracts fracture parameters such as trace length,width,and apparent dip angle,and ultimately realizes the quantitative analysis of fracture identification images.Finally,a case study of tunnel fracture was carried out in Xinjiang,China.The results show that the accuracy of fracture identification is up to 95.7%,the precision is 85.1%.And the automatic extraction of geometrical parameters of tunnel rock mass is realized.This study provides guidance for the rapid intelligent identification and geometric parameter extraction of rock fracture in underground engineering.
作者
李轶惠
许振浩
潘东东
石恒
LI Yihui;XU Zhenhao;PAN Dongdong;SHI Heng(Geotechnical and Structural Engineering Research Center,Shandong University,Shandong 250061,China;School of Qilu Transportations,Shandong University,Shandong 250061,China;School of Civil Engineering,Shandong University,Shandong 250061,China;Suzhou Research Institute,Shandong University,Suzhou 215123,China)
出处
《应用基础与工程科学学报》
EI
CSCD
北大核心
2023年第6期1427-1443,共17页
Journal of Basic Science and Engineering
基金
国家自然科学基金项目(52109129)
国家自然科学基金优秀青年科学基金项目(52022053)
江苏省自然科学基金项目(BK20210114)
山东省自然科学基金项目(ZR2021QE163)。
关键词
裂隙识别
参数提取
深度学习
隧道工程
岩体裂隙
语义分割
fracture identification
parameter extraction
deep learning
tunnel engineering
rock fracture
semantic segmentation
