Yu Lusong, Zhang Yuxing, Wang Li, Pan Qiren, Wen Yiyang
{"title":"基于机器学习方法的 CFST 柱轴向承压能力预测","authors":"Yu Lusong, Zhang Yuxing, Wang Li, Pan Qiren, Wen Yiyang","doi":"10.1007/s13296-023-00800-9","DOIUrl":null,"url":null,"abstract":"<p>Concrete-filled steel tubes (CFSTs) are widely used in engineering structures due to their excellent mechanical properties and economic benefits. This study focused on the construction of artificial neural network (ANN) models with high prediction capabilities and prediction accuracies that could predict the axial compression load capacities of short CFST columns using machine learning methods. A database was created by searching literature published over the past 40 years regarding circular-CFST bearing-capacity testing. Three ANN models with different input parameters were developed, and used the Whale Optimization Algorithm to optimize the network weights and thresholds, the core idea of which comes from the humpback whale's special bubble net attack method. Then, the predictions of the proposed machine learning models were also compared with the theoretical values produced by the formulas proposed in existing codes. The results show that the ANN models had higher accuracies and a wider application range than the existing code models. Based on the Garson's algorithm, we perform parameter sensitivity analysis on the network model to enhance the interpretability of the neural network model. Finally, a graphical user tool is built to make the strength of CFST can be predicted quickly.</p>","PeriodicalId":596,"journal":{"name":"International Journal of Steel Structures","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of the Axial Bearing Compressive Capacities of CFST Columns Based on Machine Learning Methods\",\"authors\":\"Yu Lusong, Zhang Yuxing, Wang Li, Pan Qiren, Wen Yiyang\",\"doi\":\"10.1007/s13296-023-00800-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Concrete-filled steel tubes (CFSTs) are widely used in engineering structures due to their excellent mechanical properties and economic benefits. This study focused on the construction of artificial neural network (ANN) models with high prediction capabilities and prediction accuracies that could predict the axial compression load capacities of short CFST columns using machine learning methods. A database was created by searching literature published over the past 40 years regarding circular-CFST bearing-capacity testing. Three ANN models with different input parameters were developed, and used the Whale Optimization Algorithm to optimize the network weights and thresholds, the core idea of which comes from the humpback whale's special bubble net attack method. Then, the predictions of the proposed machine learning models were also compared with the theoretical values produced by the formulas proposed in existing codes. The results show that the ANN models had higher accuracies and a wider application range than the existing code models. Based on the Garson's algorithm, we perform parameter sensitivity analysis on the network model to enhance the interpretability of the neural network model. Finally, a graphical user tool is built to make the strength of CFST can be predicted quickly.</p>\",\"PeriodicalId\":596,\"journal\":{\"name\":\"International Journal of Steel Structures\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Steel Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s13296-023-00800-9\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Steel Structures","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s13296-023-00800-9","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Prediction of the Axial Bearing Compressive Capacities of CFST Columns Based on Machine Learning Methods
Concrete-filled steel tubes (CFSTs) are widely used in engineering structures due to their excellent mechanical properties and economic benefits. This study focused on the construction of artificial neural network (ANN) models with high prediction capabilities and prediction accuracies that could predict the axial compression load capacities of short CFST columns using machine learning methods. A database was created by searching literature published over the past 40 years regarding circular-CFST bearing-capacity testing. Three ANN models with different input parameters were developed, and used the Whale Optimization Algorithm to optimize the network weights and thresholds, the core idea of which comes from the humpback whale's special bubble net attack method. Then, the predictions of the proposed machine learning models were also compared with the theoretical values produced by the formulas proposed in existing codes. The results show that the ANN models had higher accuracies and a wider application range than the existing code models. Based on the Garson's algorithm, we perform parameter sensitivity analysis on the network model to enhance the interpretability of the neural network model. Finally, a graphical user tool is built to make the strength of CFST can be predicted quickly.
期刊介绍:
The International Journal of Steel Structures provides an international forum for a broad classification of technical papers in steel structural research and its applications. The journal aims to reach not only researchers, but also practicing engineers. Coverage encompasses such topics as stability, fatigue, non-linear behavior, dynamics, reliability, fire, design codes, computer-aided analysis and design, optimization, expert systems, connections, fabrications, maintenance, bridges, off-shore structures, jetties, stadiums, transmission towers, marine vessels, storage tanks, pressure vessels, aerospace, and pipelines and more.