Journal of Yangtze River Scientific Research Institute ›› 2022, Vol. 39 ›› Issue (2): 141-146.DOI: 10.11988/ckyyb.20201119

• HYDRAULIC STRUCTURE AND MATERIAL • Previous Articles     Next Articles

Dynamically Predicting Temperature Change in the First Phase Temperature Control Stage for High Arch Dam Concrete

HUANG Jian-wen1,2, LI Fei-xiang1, YUAN Hua3, WANG Xing-xia1,2, JIANG Yi-yuan4, YE Lin-hua4   

  1. 1. College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China;
    2. Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China;
    3. China Water Resources Beifang Investigation, Design and Research Co., Ltd., Tianjin 300222, China;
    4. China Gezhouba Group Three Gorges Construction Engineering Co., Ltd., Yichang 443000, China
  • Received:2020-11-02 Revised:2021-03-05 Published:2022-02-01 Online:2022-02-11

Abstract: The aim of this research is to obtain in advance the short-term temperature change trend of concrete in the first stage of temperature control, and take corresponding temperature control measures in time to prevent from temperature cracks. With high arch dam concrete in construction period as the research object, we examined the comprehensive influences of initial temperature, water cooling, adiabatic temperature rise, environmental temperature and layer heat dissipation on the concrete temperature of pouring warehouse, and then established a dynamic prediction model of temperature change in the first stage temperature control stage of high arch dam concrete by timely updating the initial temperature. Furthermore, in view of the difference of concrete in different pouring warehouses, we adopted the nonlinear optimization method to optimize the important parameters of the model, and verified the accuracy of the model using such indicators as maximum absolute error (MAE), average absolute error (AAE) and relative error (RE). With engineering practice as case study, we updated the initial temperature with 2 days as the step and optimized the model parameters, and predicted the concrete temperature of the pouring warehouse with 12 days as the age of concrete. The maximum absolute error (MAE) between predicted value and measured value is within 0.6 ℃, the average absolute error (AAE) within 0.2 ℃, and the relative error (RE) within 0.9%. The prediction accuracy of the model meets the requirements of the construction site.

Key words: concrete, first stage of temperature control, water cooling, adiabatic temperature rise, dynamic prediction, nonlinear optimization

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