A slagimage acquisition system of tunnel
boring machines(TBMs) is developed herein to predict the surrounding rock
conditions at the excavation face in advance based on the characteristics of
rock slag without halting the TBM. Slag images are captured under various
surrounding rock conditions and processed using binarization and watershed
segmentation methods. The curvature coefficient, nonuniformity coefficient,
maximum particle size, and roughness index are used as slag grading
characteristic indices. Eight models, namely linear, logarithmic, inverse,
quadratic, power, compound, Scurve, and growth, are
employed to establish relationships among rockslag
grading parameters and the types, uniaxial compressive strengths, and integrity
coefficients of surrounding rocks. The results indicate the following: (1) The
curvature coefficient of slag decreases while the nonuniformity coefficient,
maximum particle size, and roughness index increase as surrounding rock
conditions deteriorate. (2) The curvature coefficient of rock slag increases
while the nonuniformity coefficient, maximum particle size, and roughness index
decrease with increasing uniaxial compressive strength of rock. (3) The
curvature coefficient of rock slag increases while the nonuniformity
coefficient, maximum particle size, and roughness index decrease with
increasing rock integrity coefficient.
ZHOU Zhenliang
,
LEI Ke
,
LI Qinglou
,
XIAO Haihui
,
SU Shan
. Correlation Between Surrounding Rock Conditions and
RockSlag Grading of Tunnels Bored Using Tunnel
Boring Machines Via Image Recognition[J]. Tunnel Construction, 2024
, 44(5)
: 964
-972
.
DOI: 10.3973/j.issn.2096-4498.2024.05.005
