In this study, a large number of deepburied
rockburst tunnel data are collected. The causes, processes, and characteristics
of largearea rockburst that occurred in existing and underconstruction tunnels are statistically analyzed, and the rockburst
trends, prediction methods, and disposal measures are summarized and analyzed.
The drawn conclusions are presented as follows. (1) In the rockburst prediction
stage, the rockburstclassification discriminant based
on the physicomechanical properties is universal to a
certain extent, and the criterion value can be applied to tunnels with similar
lithology and depth. The accuracy and characteristics of the prediction results
can be checked using multiple discriminants, and the fieldmonitoring measurement provides an important reference and
validation for the establishment of the criterion. (2) The stressrelief methods of different rockburst grades in the prevention and
control stage are water sprinkling for the slightrockburst
section and stress release hole for the strongrockburst
section. (3) The primary support parameters of surrounding rocks with different
grades are summarized, and based on the comparison and summary of the drilling
and blasting methods, the excavation methods, advancement length, and
corresponding waiting time of the differentrock
blasting sections are proposed. (4) It is recommended that the acquisition of
rockburst prediction information and the entire monitoring of the displacement
and strain fields in the rockburst process can be realized using the
technologies of machine learning, large database, unmanned aerial vehicle,
machine vision, signal acquisition, and multispectral and thermal imaging. A
new simulation method for rockburst process using discreteelement energy iteration algorithm is proposed.
WANG Ke
. Overview of StateofArt
of Rockburst Prediction and Prevention Techniques for
Deepburied Tunnels[J]. Tunnel Construction, 2021
, 41(2)
: 212
-224
.
DOI: 10.3973/j.issn.2096-4498.2021.02.007
