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}
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.
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.