使用耦合机器学习方法预测硫酸盐侵蚀下混凝土的抗压强度

IF 1.7 4区 工程技术 Q3 ENGINEERING, CIVIL
Libing Jin, Peng Liu, Tai Fan, Tian Wu, Yuhang Wang, Qiang Wu, Pengfei Xue, Pin Zhou
{"title":"使用耦合机器学习方法预测硫酸盐侵蚀下混凝土的抗压强度","authors":"Libing Jin, Peng Liu, Tai Fan, Tian Wu, Yuhang Wang, Qiang Wu, Pengfei Xue, Pin Zhou","doi":"10.1007/s40996-024-01544-0","DOIUrl":null,"url":null,"abstract":"<p>One of the most significant factors affecting the durability of concrete is sulfate attack. In this paper, to predict the compressive strength (CS) of concrete under sulfate attack, three coupled machine learning methods (SSA-BP, PSO-BP and NGO-BP) were developed by coupling BP neural networks (BPNN) with three swarm intelligence algorithms, which are sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO) and northern goshawk optimization algorithm (NGO), respectively. Twelve influencing factors related to material composition, erosion medium and exposure conditions are chosen as inputs, and the CS of concrete subject to sulfate attack is selected as the output. The database of 591 samples collected from published literatures is divided into three parts. Performance indexes are used to evaluate the three coupled models and BP independent model. Finally, the influence of each input on the CS of concrete under sulfate attack is examined using the Grey relational analysis approach. The following findings are reached: (1) all coupled models can predict the CS of concrete under sulfate attack with higher accuracy and achieve better performance than BP independent model, and the best one is SSA-BP model. Benefitted both from the strong nonlinear mapping ability of BPNN and from the global search and fast convergence ability of SSA, SSA-BP model has strong potential in predicting the CS of sulfate attack concrete. (2) Grey relational analysis shows that, among the twelve inputs considered, the initial compressive strength of concrete has the highest correlation (almost one) with the CS of concrete under sulfate attack. The robustness of the suggested model is confirmed by the relational analysis of all input parameters. (3) In addition, this model can provide an innovative way to assess the durability of concrete under complex or harsh environmental conditions.</p>","PeriodicalId":14550,"journal":{"name":"Iranian Journal of Science and Technology, Transactions of Civil Engineering","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods\",\"authors\":\"Libing Jin, Peng Liu, Tai Fan, Tian Wu, Yuhang Wang, Qiang Wu, Pengfei Xue, Pin Zhou\",\"doi\":\"10.1007/s40996-024-01544-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>One of the most significant factors affecting the durability of concrete is sulfate attack. In this paper, to predict the compressive strength (CS) of concrete under sulfate attack, three coupled machine learning methods (SSA-BP, PSO-BP and NGO-BP) were developed by coupling BP neural networks (BPNN) with three swarm intelligence algorithms, which are sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO) and northern goshawk optimization algorithm (NGO), respectively. Twelve influencing factors related to material composition, erosion medium and exposure conditions are chosen as inputs, and the CS of concrete subject to sulfate attack is selected as the output. The database of 591 samples collected from published literatures is divided into three parts. Performance indexes are used to evaluate the three coupled models and BP independent model. Finally, the influence of each input on the CS of concrete under sulfate attack is examined using the Grey relational analysis approach. The following findings are reached: (1) all coupled models can predict the CS of concrete under sulfate attack with higher accuracy and achieve better performance than BP independent model, and the best one is SSA-BP model. Benefitted both from the strong nonlinear mapping ability of BPNN and from the global search and fast convergence ability of SSA, SSA-BP model has strong potential in predicting the CS of sulfate attack concrete. (2) Grey relational analysis shows that, among the twelve inputs considered, the initial compressive strength of concrete has the highest correlation (almost one) with the CS of concrete under sulfate attack. The robustness of the suggested model is confirmed by the relational analysis of all input parameters. (3) In addition, this model can provide an innovative way to assess the durability of concrete under complex or harsh environmental conditions.</p>\",\"PeriodicalId\":14550,\"journal\":{\"name\":\"Iranian Journal of Science and Technology, Transactions of Civil Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iranian Journal of Science and Technology, Transactions of Civil Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40996-024-01544-0\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Science and Technology, Transactions of Civil Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40996-024-01544-0","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

摘要

影响混凝土耐久性的最重要因素之一是硫酸盐侵蚀。本文通过将 BP 神经网络(BPNN)与三种蜂群智能算法(分别为麻雀搜索算法(SSA)、粒子群优化算法(PSO)和北戈沙克优化算法(NGO))耦合,开发了三种耦合机器学习方法(SSA-BP、PSO-BP 和 NGO-BP)来预测混凝土在硫酸盐侵蚀下的抗压强度(CS)。选择与材料成分、侵蚀介质和暴露条件有关的 12 个影响因素作为输入,并选择受硫酸盐侵蚀的混凝土 CS 作为输出。从已发表的文献中收集的 591 个样本数据库分为三个部分。采用性能指标对三个耦合模型和 BP 独立模型进行评估。最后,使用灰色关系分析方法考察了各输入对硫酸盐侵蚀下混凝土 CS 的影响。得出以下结论:(1) 与 BP 独立模型相比,所有耦合模型都能以更高的精度预测硫酸盐侵蚀下混凝土的 CS,并取得更好的性能,其中最好的是 SSA-BP 模型。既得益于 BPNN 强大的非线性映射能力,又得益于 SSA 的全局搜索和快速收敛能力,SSA-BP 模型在预测硫酸盐侵蚀混凝土 CS 方面具有很强的潜力。(2) 灰色关系分析表明,在所考虑的 12 个输入中,混凝土初始抗压强度与硫酸盐侵蚀混凝土 CS 的相关性最高(接近 1)。对所有输入参数的关系分析证实了所建议模型的稳健性。(3) 此外,该模型可为评估复杂或恶劣环境条件下混凝土的耐久性提供一种创新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

Compressive Strength Prediction of Concrete Under Sulfate Attack Using Coupled Machine Learning Methods

One of the most significant factors affecting the durability of concrete is sulfate attack. In this paper, to predict the compressive strength (CS) of concrete under sulfate attack, three coupled machine learning methods (SSA-BP, PSO-BP and NGO-BP) were developed by coupling BP neural networks (BPNN) with three swarm intelligence algorithms, which are sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO) and northern goshawk optimization algorithm (NGO), respectively. Twelve influencing factors related to material composition, erosion medium and exposure conditions are chosen as inputs, and the CS of concrete subject to sulfate attack is selected as the output. The database of 591 samples collected from published literatures is divided into three parts. Performance indexes are used to evaluate the three coupled models and BP independent model. Finally, the influence of each input on the CS of concrete under sulfate attack is examined using the Grey relational analysis approach. The following findings are reached: (1) all coupled models can predict the CS of concrete under sulfate attack with higher accuracy and achieve better performance than BP independent model, and the best one is SSA-BP model. Benefitted both from the strong nonlinear mapping ability of BPNN and from the global search and fast convergence ability of SSA, SSA-BP model has strong potential in predicting the CS of sulfate attack concrete. (2) Grey relational analysis shows that, among the twelve inputs considered, the initial compressive strength of concrete has the highest correlation (almost one) with the CS of concrete under sulfate attack. The robustness of the suggested model is confirmed by the relational analysis of all input parameters. (3) In addition, this model can provide an innovative way to assess the durability of concrete under complex or harsh environmental conditions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.30
自引率
11.80%
发文量
203
期刊介绍: The aim of the Iranian Journal of Science and Technology is to foster the growth of scientific research among Iranian engineers and scientists and to provide a medium by means of which the fruits of these researches may be brought to the attention of the world’s civil Engineering communities. This transaction focuses on all aspects of Civil Engineering and will accept the original research contributions (previously unpublished) from all areas of established engineering disciplines. The papers may be theoretical, experimental or both. The journal publishes original papers within the broad field of civil engineering which include, but are not limited to, the following: -Structural engineering- Earthquake engineering- Concrete engineering- Construction management- Steel structures- Engineering mechanics- Water resources engineering- Hydraulic engineering- Hydraulic structures- Environmental engineering- Soil mechanics- Foundation engineering- Geotechnical engineering- Transportation engineering- Surveying and geomatics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信