In order to study the complex relationships among
tunneling parameters of superlarge diameter slurry shield in
composite strata, a prediction model of tunneling parameters based on BP neural
network and 700ring tunneling data of Shantou Bay
Tunnel Project is established. By optimizing function category and network
structure, the prediction model can predict cutterhead torque, cutterhead
energy consumption and average slurry pressure more accurately and
quantitatively. The results show that: (1) In composite strata, the absolute
value of the Pearson productmoment correlation
coefficient between penetration and tunneling speed, that between penetration
and cutterhead rotation speed, and that between cuttehead current and
cutterhead rotation speed are all above 0.75, which have good linear
correlation; the relationships among other tunneling parameters are not clear.
(2) In composite strata, the average errors of cutterhead torque and energy
consumption between the predicted values and monitored data are about 5%, that
of slurry pressure between predicted value and monitored data is 1.31, which
indicates that the prediction results have high precision and can meet the
requirements of shield tunneling. (3) The prediction results of tunneling
parameters in soft soil stratum have been further improved by the prediction
model based on BP neural network. (4) By quantitatively predicting other
tunneling parameters in prediction model based on BP neural network, the
operation is simplified, and the prediction effect is good, which can provide
reference for shield driver, improve the construction efficiency and even
realize intelligentized shield tunneling.
FAN Wenchao
,
SUN Zhenchuan
,
LI Fengyuan
,
ZHANG Bing
,
CHEN Qiao
,
WANG Famin
,
WANG Kai
. Research on Prediction of Tunneling Parameters of
Superlarge Diameter Slurry Shield in Composite
Strata of Shantou Bay Tunnel[J]. Tunnel Construction, 2020
, 40(8)
: 1160
-1168
.
DOI: 10.3973/j.issn.2096-4498.2020.08.008
