Guo Shaoqiang, Kou Honggang, Bi Yuzhang, Mamlooki Mina
Civil Engineering College of Xijing University, Xi'an, 710000, Shaanxi, China.
College of Resources and Environment, Fuzhou Agriculture and Forestry University, Fuzhou, 350000, Fujian, China.
Sci Rep. 2024 Aug 29;14(1):20080. doi: 10.1038/s41598-024-71122-x.
The compressive strength of concrete depends on various factors. Since these parameters can be in a relatively wide range, it is difficult for predicting the behavior of concrete. Therefore, to solve this problem, an advanced modeling is needed. The aim of the literature is to achieve an ideal and flexible solution for predicting the behavior of concrete. Therefore, it is necessary to develop new approaches. Artificial Neural Networks (ANNs) have evolved from a theoretical method to a widely utilized technology by successful applications for a variety of issues. Actually, ANNs are a strong computing tool that provides the right solutions to problems that are difficult to use conventional methods. Inspired by the biological neural system, these networks are now widely used for solving a wide range of complicated problems in civil engineering. This study''s target is evaluating the performance of developed African vulture optimization algorithm (DAVOA)-Elman neural networks (ENNs) by considering different input parameters in predicting the self-compacting concrete compressive strength. Hence, once 8 parameters and again to get as close as possible to the prediction conditions in the laboratory, 140 parameters entered to the improved version of Elman Neural Networks as input. According to the results, the element network has the lowest mean squares of the test error in predicting the compressive strength of 7 and 28 days in 100 repetitions. Further, in predicting both compressive strengths, the element grid with the Logsig-Purelin interlayer transfer function has the lowest test error, which determines the optimal transfer function. Moreover, the results showed that DAVOA as a reliable tool with time and cost savings have high power in predicting the desired characteristics. Also, in predicting both 7-day and 28-day compressive strength, networks built with 140 parameters have a 74.54 and 70.44% improvement in test error over 8-parameter networks, respectively, which directly affects this effect. Further parameters are considered as input to the network error rate in predicting the desired properties.
混凝土的抗压强度取决于多种因素。由于这些参数的范围可能相对较宽，因此很难预测混凝土的性能。因此，为了解决这个问题，需要先进的建模方法。文献的目的是为预测混凝土性能找到理想且灵活的解决方案。因此，有必要开发新的方法。人工神经网络（ANNs）已从一种理论方法发展成为一种通过成功应用于各种问题而被广泛使用的技术。实际上，人工神经网络是一种强大的计算工具，能为难以用传统方法解决的问题提供正确的解决方案。受生物神经系统的启发，这些网络现在被广泛用于解决土木工程中的各种复杂问题。本研究的目标是通过考虑不同输入参数，评估改进的非洲秃鹫优化算法（DAVOA）-埃尔曼神经网络（ENNs）在预测自密实混凝土抗压强度方面的性能。因此，为了尽可能接近实验室的预测条件，先是输入8个参数，然后又将140个参数输入到改进版的埃尔曼神经网络作为输入。结果表明，在100次重复预测7天和28天抗压强度时，单元网络的测试误差均方最低。此外，在预测两种抗压强度时，具有Logsig-Purelin层间传递函数的单元网格测试误差最低，这确定了最优传递函数。而且，结果表明DAVOA作为一种可靠的工具，在节省时间和成本方面具有强大的预测所需特性的能力。同样，在预测7天和28天抗压强度时，采用140个参数构建的网络与采用8个参数的网络相比，测试误差分别降低了74.54%和70.44%，这直接影响了该效果。进一步将参数作为网络输入来预测所需性能时的误差率。
