利用各种人工神经网络估算螺旋钢筋混凝土柱的极限弯矩承载力

Q1 Engineering
Faroq Maraqa, Jamal Al Adwan, Yazan Alzubi, Bilal Yasin, Ahmed Khatatbeh
{"title":"利用各种人工神经网络估算螺旋钢筋混凝土柱的极限弯矩承载力","authors":"Faroq Maraqa, Jamal Al Adwan, Yazan Alzubi, Bilal Yasin, Ahmed Khatatbeh","doi":"10.15866/irece.v14i4.22143","DOIUrl":null,"url":null,"abstract":"Over the last few decades, intensive investigations on the artificial neural network capabilities for addressing structural engineering problems have been concluded in the literature. Multiple models for predicting the load-bearing capacity and failure mode have been developed in this regard. However, most of the studies on the capabilities of artificial neural networks for estimating the ultimate moment capacity were focused on the feedforward backpropagation approach. As a result, this research aims to investigate the performance of using different artificial neural network approaches to forecast the ultimate moment capacity of spiral RC columns. As a part of the study, the performance of feedforward backpropagation, cascade-forward neural networks, and generalized regression neural networks will be compared and evaluated against experimental and traditional results. The findings demonstrated that artificial neural networks provide a reliable method for forecasting the spiral RC columns' moment capacity, and they can outweigh code-based empirical formulation.","PeriodicalId":37854,"journal":{"name":"International Review of Civil Engineering","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating Ultimate Moment Capacity of Spirally Reinforced Concrete Columns Using Various Artificial Neural Networks\",\"authors\":\"Faroq Maraqa, Jamal Al Adwan, Yazan Alzubi, Bilal Yasin, Ahmed Khatatbeh\",\"doi\":\"10.15866/irece.v14i4.22143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over the last few decades, intensive investigations on the artificial neural network capabilities for addressing structural engineering problems have been concluded in the literature. Multiple models for predicting the load-bearing capacity and failure mode have been developed in this regard. However, most of the studies on the capabilities of artificial neural networks for estimating the ultimate moment capacity were focused on the feedforward backpropagation approach. As a result, this research aims to investigate the performance of using different artificial neural network approaches to forecast the ultimate moment capacity of spiral RC columns. As a part of the study, the performance of feedforward backpropagation, cascade-forward neural networks, and generalized regression neural networks will be compared and evaluated against experimental and traditional results. The findings demonstrated that artificial neural networks provide a reliable method for forecasting the spiral RC columns' moment capacity, and they can outweigh code-based empirical formulation.\",\"PeriodicalId\":37854,\"journal\":{\"name\":\"International Review of Civil Engineering\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Review of Civil Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15866/irece.v14i4.22143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Civil Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15866/irece.v14i4.22143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

在过去的几十年里,对人工神经网络解决结构工程问题的能力的深入研究已经在文献中得到了总结。在这方面已经发展了多种预测承载力和破坏模式的模型。然而,大多数关于人工神经网络极限弯矩容量估计能力的研究都集中在前馈反向传播方法上。因此,本研究旨在探讨使用不同的人工神经网络方法来预测螺旋钢筋混凝土柱的极限弯矩承载力的性能。作为研究的一部分,前馈反向传播、级联前向神经网络和广义回归神经网络的性能将与实验结果和传统结果进行比较和评估。研究结果表明,人工神经网络提供了一种可靠的方法来预测螺旋钢筋混凝土柱的弯矩容量,并且它优于基于代码的经验公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Ultimate Moment Capacity of Spirally Reinforced Concrete Columns Using Various Artificial Neural Networks
Over the last few decades, intensive investigations on the artificial neural network capabilities for addressing structural engineering problems have been concluded in the literature. Multiple models for predicting the load-bearing capacity and failure mode have been developed in this regard. However, most of the studies on the capabilities of artificial neural networks for estimating the ultimate moment capacity were focused on the feedforward backpropagation approach. As a result, this research aims to investigate the performance of using different artificial neural network approaches to forecast the ultimate moment capacity of spiral RC columns. As a part of the study, the performance of feedforward backpropagation, cascade-forward neural networks, and generalized regression neural networks will be compared and evaluated against experimental and traditional results. The findings demonstrated that artificial neural networks provide a reliable method for forecasting the spiral RC columns' moment capacity, and they can outweigh code-based empirical formulation.
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
2.90
自引率
0.00%
发文量
34
期刊介绍: The International Review of Civil Engineering (IRECE) is a peer-reviewed journal that publishes original theoretical papers, applied papers, review papers and case studies on all fields of civil engineering. The scope of the Journal encompasses, but is not restricted to the following areas: infrastructure engineering; transportation engineering; structural engineering (buildings innovative structures environmentally responsive structures bridges stadiums commercial and public buildings, transmission towers, television and telecommunication masts, cooling towers, plates and shells, suspension structures, smart structures, nuclear reactors, dams, pressure vessels, pipelines, tunnels and so on); earthquake, hazards, structural dynamics, risks and mitigation engineering; environmental engineering; structure-fluid-soil interaction; wind engineering; fire engineering; multi-scale analysis; constitutive modeling and experimental testing; construction materials; composite materials in engineering structures (use, theoretical analysis and fabrication techniques); novel computational modeling techniques; engineering economics. The Editorial policy is to maintain a reasonable balance between papers regarding different research areas so that the Journal will be useful to all interested scientific groups.
×
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