There is a lack of prediction and earlywarning
methods for surrounding rock quality and collapse risk when using a tunnel
boring machine(TBM). TBM boring big data are mined to address this, and the
collapse data are analyzed to provide auxiliary judgment criteria for potential
collapse risks. Redundant and continuous raw data are preprocessed to obtain
highquality analytical data. A method for calculating
characteristic rock parameters based on parameter correlation analysis is
proposed. The rationality and applicability of these parameters are
demonstrated through simplified theoretical derivations, indoor test results,
and onsite boring tests. Finally, based on actual TBM
collapse cases, the correlation between the characteristic parameters and
surrounding rock geological conditions is analyzed, providing a basis for the
rapid assessment of collapse risk. The results show that the characteristic
parameters of the surrounding rock obtained from processing and analysis of the
TBM boring data reflect the quality of the surrounding rock, and their values
are positively correlated with the quality of the surrounding rock. When their
values significantly decrease and the variation exceeds 69.2%, the current
boring cycle is highly prone to potential collapse risk.
PEI Chengyuan
,
ZHANG Yunpei
,
LIU Junsheng
,
LIU Lipeng
,
CAO Ruilang
. Collapse Analysis in Water-Diversion Tunnels Based on Boring Big Data and Characteristic Parameters of Tunnel Boring Machine[J]. Tunnel Construction, 2024
, 44(5)
: 952
-963
.
DOI: 10.3973/j.issn.2096-4498.2024.05.004
