A dynamic classification method is developed
based on the actual revealed conditions of the surrounding rock of tunnels
during tunnel construction. This method can reduce economic losses, safety
accidents, and other problems caused by differences between the actual
classification of surrounding rock and geological surveying results. First, a
case study is conducted on the Muzhailing tunnel on the Weiyuan-Wudu expressway in Gansu, China; tunnel face images are collected
using a DSLR camera, and a T-ResNet model is designed
based on the deep-learning ResNet18 network to identify
the features of the tunnel face rock images qualitatively. Then, by leveraging
digital image processing technology to quantitatively extract joint and
fracture characteristics, the integrity index of the tunnel face is determined.
Finally, a rock quality assessment index system is established based on indices
of rock hardness, rock mass integrity, main structural plane occurrence,
groundwater development condition, initial ground stress, and joint
expansibility. Thus, an intelligent dynamic classification of tunnel rock is
achieved using the feature-weighted K-nearest neighbors(KNN) algorithm. The results demonstrate the
following: (1) The accuracies of the T-ResNet model in
the joint fracture test set and the groundwater test set reach 83.23% and
92.86%, respectively, showing its feasibility and high precision in surrounding
rock recognition. (2) The machine vision method used in processing the on-site surrounding rock images can rapidly extract the rock mass
integrity coefficient and groundwater development condition, realizing
intelligent and efficient analysis. (3) The KNN intelligent dynamic
classification method has good applicability and high accuracy in the case
study.
ZHOU Menglin
,
CHEN Qiang
,
WANG Bo
,
SONG Ziyuan
,
PENG Chuanyang
,
CHENG Li
. Intelligent Dynamic
Classification of Surrounding Rock of Highway Tunnels Based on Image Recognition[J]. Tunnel Construction, 2024
, 44(6)
: 1274
-1282
.
DOI: 10.3973/j.issn.2096-4498.2024.06.014
