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Open Access

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

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
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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 modelingtemperature forecastmass concretecooling control

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Tsinghua Science and Technology
Volume 28 Issue 3,
June 2023
Pages 511-524
DOI: 10.26599/TST.2022.9010015
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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Received: 19 February 2022
Revised: 27 May 2022
Accepted: 06 June 2022
Published: 13 December 2022
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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