An intelligent surrounding
rock grade identification method based on the fusion of drilling parameters and
tunnel seismic prediction (TSP) method is proposed to improve the
identification accuracy of surrounding rock grade based on geological
information in tunnel survey and construction stages. Drilling parameters, TSP
reports, and geological sketches are collected from the construction site to
interpret corresponding data and surrounding rock information. Then, five
fusion scenarios are set and four machine leaning classification algorithms,
i.e., K-nearest neighbor, gradient
boosting tree, random forest, and extreme tree, are employed to verify the
separability of fused data for determining the final fusion mode of the
drilling parameters and TSP data. To determine the impact of various data
partitioning methods on the fitting ability of models, 500 partitioning modes
are applied to the dataset by setting random number seeds to fully fit each
model. Results show that the extreme tree model yields the best surrounding
rock grade classification effect with a maximum accuracy of 91.3% by fusing
drilling parameters such as feed rate, thrust pressure, impact pressure, and
rotation pressure with the longitudinal and transverse wave velocities in TSP,
respectively. Finally, the Bayesian optimization algorithm is used to optimize
the hyperparameters of the extreme tree model. The optimized model exhibits
improved classification performance for various rock grades, with overall and
increase accuracies of 93.27%
and 1.93%, respectively.
XU Kunjie
,
ZHANG Zhirong
,
XIANG Lulu
,
CHENG Haibing
,
TONG Jianjun
,
YE Pei
,
MIAO Xingwang
. Classification and
Prediction Model of Tunnel Surrounding Rock Grade by Integrating Drilling Parameters and Tunnel Seismic Prediction Data[J]. Tunnel Construction, 2025
, 45(3)
: 579
-586
.
DOI: 10.3973/j.issn.2096-4498.2025.03.013
