Prediction and analysis of temperature recovery after arch closure grouting of Baihetan Arch Dam

Abstract

Arch closure grouting is an essential procedure for attaining structural completion during the construction of a concrete arch dam. After the closure, due to the continuous heat emission from cement hydration, the internal temperature of concrete rises rapidly, which affects stress distribution and structural stability. In order to accurately predict the temperature evolution of concrete pouring blocks after arch closure, this paper conducted a comparative study using neural networks and finite element methods. First, a hybrid model, CNN-BiLSTM, was constructed. This model integrates a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM). The Weighted Mean of Vectors algorithm (INFO) was then introduced to optimize the model parameters. The temperature variation trend of concrete pouring blocks after arch closure was predicted using this approach. Simultaneously, considering the factors such as external temperature, cooling water, adiabatic temperature rise and concrete age, a three-dimensional finite element model of concrete pouring blocks was established to simulate the temperature field distribution of concrete. The comparison results indicate that both methods can achieve the prediction accuracy required by the project (with an error of less than 2 °C). Among them, the finite element simulation performs better in terms of stability (with a difference of less than 1 °C from the measured value). At the same time, the INFO-CNN-BiLSTM model exhibits significant temperature fluctuations during certain periods and demonstrates insufficient generalization ability. However, it offers the advantage of high computational efficiency.