(College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China)
To quickly and accurately predict the temperature history during the concrete construction period, a prediction model based on the long short-term memory (LSTM) network algorithm was proposed combined with the principal component analysis (PCA).Taking the baseboard of the Siyaogang Sluice and Bayaogang Sluice in Shanghai Chongming Island as an example, the PCA method was used to reduce the number of the possible influencing factors of the concrete temperature field, and then the LSTM prediction model of temperature history based on the temperature data of the Siyaogang Sluice baseboard was established to train the input principal components. Then the trained model was used for the fitting and prediction of the temperature history of the Bayaogang Sluice and was compared with the measured results. The results show that the predicted value fits well with the measured one with the RSM error within 2℃ and the coefficient of determination close to 1, meeting the requirements of engineering accuracy. The proposed method can partially be an alternative for FEM back-analysis, which can increase the efficiency for concrete temperature field prediction of pump and sluice projects.
程井,孔垂穗,邹科辉.基于LSTM的泵闸工程混凝土施工期温度场预测[J].水利水电科技进展,2023,43(2):76-81.(CEHNG Jing, KONG Chuisui, ZOU Kehui. Temperature field prediction during concrete construction period of pump and sluice project based on LSTM[J]. Advances in Science and Technology of Water Resources,2023,43(2):76-81.(in Chinese))Copy