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

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.