Abstract: 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 largescale 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 multiparameter and nonlinear fitting ability of two deep learning algorithms, namely, the long shortterm memory (LSTM) and the gate recurrent unit (GRU). Relying on the onsite concrete temperature measurement data of the doublegrating 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).

Key words: mass concrete, deep learning, concrete pouring, temperature prediction, opencut tunnel