The internal temperature change in mass tunnel
concrete can be investigated in real time by formulating timely and reasonable
temperature control measures that can prevent the temperature cracks caused by
the largescale hydration heat reaction during the pouring process of mass
tunnel concrete. A rolling prediction method is proposed for the internal
temperature change of mass tunnel concrete based on the multiparameter and nonlinear fitting ability of two deep learning
algorithms, namely, the long shortterm memory (LSTM)
and the gate recurrent unit (GRU). Relying on the onsite
concrete temperature measurement data of the doublegrating
sensor used to construct the Sanjiang Middle road connecting tunnel project in
Quzhou Smart New City, the mean absolute error (MAE) and the 〖WT５《TNR#I》〗R〖WT５《TNR》〗squared (〖WT５《TNR#I》〗R〖WT５《TNR》〗2) values
are used herein to test and evaluate the prediction result accuracies of the
two models. The results show that the two network models can capture the
internal temperature development law of mass tunnel concrete and accurately
predict its internal temperature change curve. Furthermore, the GRU accuracy is
better than that of LSTM. The MAE of GRU is 1.34 ℃,
which is 1.07 ℃ lower than that of LSTM, and the 〖WT５《TNR#I》〗R〖WT５《TNR》〗2 of GRU
is 0.98, which is better than that of LSTM (i.e., 0.9).
FAN Zhongjing
,
ZHENG Chenlu
. Rolling Prediction Method of Tunnel Concrete
Temperature Based on Deep Learning[J]. Tunnel Construction, 2023
, 43(4)
: 611
-617
.
DOI: 10.3973/j.issn.2096-4498.2023.04.007
