University of Transport Technology, Hanoi, Vietnam.
PLoS One. 2021 Dec 3;16(12):e0260847. doi: 10.1371/journal.pone.0260847. eCollection 2021.
An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8-14-4-1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.
本研究使用了一个广泛的仿真程序，旨在发现预测含有矿渣微粉（GGBFS）的混凝土抗压强度的最佳 ANN 模型。为此，从文献中编译了一个包含 595 个样本的实验数据库，用于寻找最佳 ANN 架构。该数据库的八个输入分别是水泥含量、水含量、粗骨料含量、细骨料含量、矿渣微粉含量、羧酸型高效减水剂含量、高效减水剂含量和测试龄期。因此，采用常规统计指标对 ANN 设计的最优选择进行了评估和评价。结果表明，在所研究的 240 种架构中，采用最佳架构 [8-14-4-1] 和最佳 ANN 模型，可以非常有效地预测使用矿渣微粉的混凝土抗压强度，在训练部分的最大 R2 值为 0.968，在测试部分为 0.965。此外，还使用最佳 ANN 模型进行了 500 次蒙特卡罗模拟的敏感性分析，以确定 ANN 模型在预测混凝土抗压强度方面的可靠性。本研究的结果可能使 ANN 模型在许多土木工程挑战中的应用更加容易和高效。
