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Tsinghua Science and Technology 2023, 28(3): 511-524 https://doi.org/10.26599/TST.2022.9010015
Open Access | Issue | Published: 13 December 2022

An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control

Show Author's Information Ming Li1,  Peng Lin1( ),  Daoxiang Chen1,  Zichang Li2,  Ke Liu3,  Yaosheng Tan3
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China
China Three Gorges Construction Engineering Corporation, Chengdu 610000, China
Keywords:
artificial neural networks (ANN), predictive modeling, temperature forecast, mass concrete, cooling control
Cite this article:
Li M, Lin P, Chen D, et al. An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control. Tsinghua Science and Technology, 2023, 28(3): 511-524. https://doi.org/10.26599/TST.2022.9010015
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Abstract Full text About this article

Concrete temperature control during dam construction (e.g., concrete placement and curing) is important for cracking prevention. In this study, a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks (ANN). The development workflow for the forecast model consists of data integration, data preprocessing, model construction, and model application. More than 80 000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam, which is the largest hydropower project in the world under construction. Machine learning algorithms, including ANN, support vector machines, long short-term memory networks, and decision tree structures, are compared in temperature prediction, and the ANN is determined to be the best for the forecast model. Furthermore, an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day. The root mean square error of the forecast precision is 0.15 ∘C on average. The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.


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About this article

An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control

Show Author's information Ming Li1, Peng Lin1( ), Daoxiang Chen1, Zichang Li2, Ke Liu3, Yaosheng Tan3
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China
China Three Gorges Construction Engineering Corporation, Chengdu 610000, China

Abstract

Concrete temperature control during dam construction (e.g., concrete placement and curing) is important for cracking prevention. In this study, a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks (ANN). The development workflow for the forecast model consists of data integration, data preprocessing, model construction, and model application. More than 80 000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam, which is the largest hydropower project in the world under construction. Machine learning algorithms, including ANN, support vector machines, long short-term memory networks, and decision tree structures, are compared in temperature prediction, and the ANN is determined to be the best for the forecast model. Furthermore, an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day. The root mean square error of the forecast precision is 0.15 ∘C on average. The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.

Keywords: artificial neural networks (ANN), predictive modeling, temperature forecast, mass concrete, cooling control

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Received: 19 February 2022
Revised: 27 May 2022
Accepted: 06 June 2022
Published: 13 December 2022
Issue date: June 2023

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© The author(s) 2023.

Acknowledgements

This research was supported by the China Three Gorges Corporation Research Program (Nos. WDD/0490, WDD/0578, and BHT/0805) and the National Natural Science Foundation of China (No. 51979146).

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