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 ı 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.

Intelligent ventilation-on-demand control system for the construction of underground tunnel complex

“…Owing to advancements in information technology, closed-loop control technology has been implemented in various construction sites [35]. For example, for intelligent pipe-cooling control in massive concrete [36], a short-term temperature forecast model for mass concrete cooling control was developed using ANN [37]. An intelligent grouting control system named the intelligent grout control hub (iGH) has been established to achieve one-button closed-loop intelligent control of cement grouting by developing robust grouting control models, novel grouting facilities, and intelligent online monitoring approaches [38].…”

The Influence of Wind-Induced Response in Urban Trees on the Surrounding Flow Field

“…In recent years, the turbulent airflow field around trees has been widely simulated using computational fluid dynamics (CFD) technology. Due to the complex morphological structure of trees, previous studies have simplified tree canopies into elliptical, square, rectangular and other geometries and equated them to porous media to analyze the effect of trees on the flow field [27][28][29]. It has been shown that the porosity is not constant during the flow of air [30][31][32].…”