{"title":"Axial compressive behavior of concrete-filled double-skin steel tubular columns with localized cracks","authors":"Tong Zhang , Shiqi Huang , Shan Gao","doi":"10.1016/j.istruc.2025.109302","DOIUrl":null,"url":null,"abstract":"<div><div>Concrete-filled double-skin steel tubular (CFDST) columns are widely used in a wide range of applications such as high-rise buildings, long-span bridges, offshore platforms, and heavy-duty industrial structures. However, due to the exposed steel tube, the external steel tube of CFDST columns is prone to cracking during long-term service, which affects their normal and safe use. This paper presents the mechanical behavior of CFDST columns with localized cracks. The peak load, failure mode, and full-range load-strain curve were produced according to the FE (finite element) model which were all line up with the findings of the experimental study. The impact of localized cracks on the CFDST stub struts in terms of peak load, stiffness, and flexibility is evaluated. The results indicate that the crack length, crack width, and crack depth exert minimal influence on the ultimate load and stiffness of the specimen. The crack length, crack width, and crack depth significantly impact the specimen's ductility. The ductility of the specimen is rapidly reduced by 30.9 %, especially when the steel tubes are penetrated due to corrosion. The crack angle barely impacts the breaking strength of the specimen. As the crack angle increases, the stiffness gradually decreases and the ductility gradually increases. A predictive technique of an error of less than 6 % is shown for the axial compressive capacity of CDFST pile columns with localized cracks. Finally, the GA-BP neural network model demonstrates its improved accuracy and generalization ability by comparing the genetic algorithm to a standard BP neural network, exhibiting higher the coefficient of determination and lower prediction errors on training and test datasets.</div></div>","PeriodicalId":48642,"journal":{"name":"Structures","volume":"78 ","pages":"Article 109302"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352012425011166","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Concrete-filled double-skin steel tubular (CFDST) columns are widely used in a wide range of applications such as high-rise buildings, long-span bridges, offshore platforms, and heavy-duty industrial structures. However, due to the exposed steel tube, the external steel tube of CFDST columns is prone to cracking during long-term service, which affects their normal and safe use. This paper presents the mechanical behavior of CFDST columns with localized cracks. The peak load, failure mode, and full-range load-strain curve were produced according to the FE (finite element) model which were all line up with the findings of the experimental study. The impact of localized cracks on the CFDST stub struts in terms of peak load, stiffness, and flexibility is evaluated. The results indicate that the crack length, crack width, and crack depth exert minimal influence on the ultimate load and stiffness of the specimen. The crack length, crack width, and crack depth significantly impact the specimen's ductility. The ductility of the specimen is rapidly reduced by 30.9 %, especially when the steel tubes are penetrated due to corrosion. The crack angle barely impacts the breaking strength of the specimen. As the crack angle increases, the stiffness gradually decreases and the ductility gradually increases. A predictive technique of an error of less than 6 % is shown for the axial compressive capacity of CDFST pile columns with localized cracks. Finally, the GA-BP neural network model demonstrates its improved accuracy and generalization ability by comparing the genetic algorithm to a standard BP neural network, exhibiting higher the coefficient of determination and lower prediction errors on training and test datasets.
期刊介绍:
Structures aims to publish internationally-leading research across the full breadth of structural engineering. Papers for Structures are particularly welcome in which high-quality research will benefit from wide readership of academics and practitioners such that not only high citation rates but also tangible industrial-related pathways to impact are achieved.