Wawrzeńczyk Jerzy
Faculty of Civil Engineering and Architecture, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland.
Materials (Basel). 2024 Dec 17;17(24):6157. doi: 10.3390/ma17246157.
One widely used method to predict concrete strength development based on temperature variations during curing is the equivalent maturity time (te) method. This method uses the activation energy (Ea) as its key parameter, which reflects the cement's sensitivity to temperature. However, research shows that the Ea value varies depending on factors such as cement type, water/cement ratio, temperature, and additives. The permanent subject of discussion is the question of what value of the Ea parameter should be assumed. In this paper, a new approach is proposed by using a neural network analysis method to develop a strength-temperature history model. It was assumed that the ANN-fc% = f(Q, E, T, t) model would have 4 inputs: hydration heat (Q), activation energy (Ea), temperature (T), and time (t). The research was conducted on mortars using 6 cements, at curing temperatures ranging from 5 to 35 °C, assessing strength over a 90 day period. The results showed that the ANN analysis method allows for estimating the relative compressive strength with sufficient accuracy. Analysis of the input nodes indicated that Q influences early strength gain, while Ea affects later strength development. The application of the ANN model for calculating strength based on temperature changes during maturation was illustrated.
一种基于养护过程中温度变化来预测混凝土强度发展的广泛使用的方法是等效成熟时间（te）法。该方法将活化能（Ea）作为其关键参数，活化能反映了水泥对温度的敏感性。然而，研究表明，Ea值会因水泥类型、水灰比、温度和添加剂等因素而有所不同。一直以来讨论的核心问题是应假定Ea参数为何值。本文提出了一种新方法，即使用神经网络分析方法来建立强度-温度历史模型。假定人工神经网络模型ANN-fc% = f(Q, E, T, t)有4个输入：水化热（Q）、活化能（Ea）、温度（T）和时间（t）。研究使用了6种水泥制作砂浆，在5至35°C的养护温度下进行，评估90天内的强度。结果表明，人工神经网络分析方法能够以足够的精度估算相对抗压强度。对输入节点的分析表明，Q影响早期强度增长，而Ea影响后期强度发展。文中展示了人工神经网络模型在根据成熟过程中的温度变化计算强度方面的应用。
