Onsite determination of the weathering
degree of the surrounding rock has disadvantages such as strong subjectivity
and poor accuracy. Therefore, a quantitative identification technology based on
image recognition and machine learning is developed. Based on the images of the
tunnel working surface collected from the DaqingGuangzhou
doubleline expansion project, the digital image
processing technology is applied to automatically extract the color, texture,
and joint information of the tunneling face. Next, a model for determining the
weathering degree of the surrounding rock is established by applying machine
learning to filter the extracted parameters. Conclusions drawn are as follows:
(1) The indices contributing to the accuracy of the prediction model from large
to small are color, integrity, and texture, corresponding to the prediction
accuracies of 71.91%, 70.78%, and 47.19%. (2) Compared with the single index
prediction model, the multiindex prediction model has
a higher prediction accuracy. The prediction model with three indices of color,
texture, and integrity exhibits a prediction accuracy of 86.52%. (3) Eight key
secondary indices are filtered from 13 indices, based on which a new prediction
model is constructed with a prediction accuracy of 85%, meeting the engineering
requirements. Results show that color, texture, and integrity information can
effectively represent the weathering degree of the surrounding rock.
Additionally, the prediction model based on these indices can stably and
effectively determine the weathering degree of the tunneling face.
MAO Qinping
,
LIU Juncheng
,
YANG Xiaoqiu
,
LIU Xuezeng
,
SANG Yunlong
. Method for Determining Weathering Degree of
Surrounding Rock With Multifeatures Based on Machine
Vision[J]. Tunnel Construction, 2024
, 44(4)
: 673
-681
.
DOI: 10.3973/j.issn.2096-4498.2024.04.005
