[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124147-en":3,"doc-seo-124147-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124147,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-based prediction of compressive strength in circular FRP-confined concrete columns - Original Research","Machine learning models are developed to evaluate the compressive strength of circular FRP-confined concrete columns. A curated database with 366 samples, covering both CFRP and GFRP, is constructed after systematically organizing codes and models proposed by different researchers to identify key influencing indicators. Predictive performance is assessed by comparing models derived from codes and researcher proposals, and an XGBoost-based parameter analysis is conducted. Results show XGBoost achieves the best fit and lowest variation, while FRP thickness, elastic modulus, and concrete strength emerge as major drivers.","TYPE Original Research PUBLISHED 06 June 2024  \nDOI 10.3389/fmats.2024.1408670  \nOPEN ACCESS  \nEDITED BY  \nLu Ke,  \nGuangxi University, China  \nREVIEWED BY  \nDong Guo,  \nHong Kong Polytechnic University, Hong Kong SAR, China  \nHaitao Wang,  \nHohai University, China  \n*CORRESPONDENCE  \nJiehong Li,  \n [jiehong.li@unsw.edu.au](jiehong.li@unsw.edu.au)  \nRECEIVED 28 March 2024  \nACCEPTED 10 May 2024  \nPUBLISHED 06 June 2024  \nCITATION  \nCui R, Yang H, Li J, Xiao Y, Yao G and Yu Y (2024), Machine learning-based prediction of  \ncompressive strength in circular FRP-confined concrete columns.  \nFront. Mater. 11:1408670 .  \ndoi: 10.3389/fmats.2024.1408670  \nCOPYRIGHT  \n© 2024 Cui, Yang, Li, Xiao, Yao and Yu. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of compressive strength in circular FRP-confined concrete columns  \nRuifu Cui 1,2, Huihui Yang 2, Jiehong Li 3*, Yao Xiao 4, Guowen Yao 1 and Yang Yu 3  \n1School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China, 2School of Civil Engineering, Chongqing University of Arts and Science, Chongqing, China, 3School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia, 4China Railway 21st Bureau Group Corporation Limited Fifth Engineering Co., Ltd., Lanzhou, China  \nThis research aims to evaluate the compressive strength of FRP-confined columns using machine learning models. By systematically organizing codes and models proposed by various researchers, significant indicators influencing compressive strength have been identified. A comprehensive database comprising 366 samples, including both CFRP and GFRP, has been assembled. Based on this database, a machine learning model was developed to accurately predict compressive strength. A thorough evaluation was conducted, comparing models proposed by codes and researchers. Additionally, a detailed parameter analysis was performed using the XGBoost model. The findings highlight the importance of both code-based and researcher-proposed models in enhancing our understanding of compressive strength. However, certain models show tendencies towards conservative or overestimated predictions, indicating the need for further accuracy enhancement. Among the models considered, the XGBoost model demonstrated the highest goodness of fit (0.97) and the lowest coefficient of variation (8%), making it a suitable choice for investigating compressive strength. Notable parameters significantly influencing compressive strength include FRP thickness, elastic modulus, and concrete strength.  \nKEYWORDS  \nFRP-confined columns, compressive strength, machine learning, XGBoost, prediction model  \n1 Introduction  \nThe lightweight, high-strength, and easily processable nature of Fibre Reinforced Polymer (FRP) materials make them extensively utilized for reinforcing concrete or reinforced concrete structures (Deifalla, 2022; Jedrzejko et al., 2023; Liao et al., 2023; Nadir et al., 2023; Sayed et al., 2023) . Traditionally, steel cages or steel sleeves are externally applied to concrete columns to enhance ductility and loadbearing capacity (Richart et al., 1929; Ruiz-Pinilla et al., 2021; Salah et al., 2022; Truong et al., 2022) . However, steel cages increase the self-weight and crosssectional area of the structure, whereas steel sleeves have a comparatively lesser impact on self-weight and cross-sectional area. Additionally, steel structures are  \nFrontiers in Materials 01 [frontiersin.org](frontiersin.org)  \nvulnerable to environmental factors and corrosion. Furthermore, both methods are","cbCaink4BRgJGol4","https://ap.wps.com/l/cbCaink4BRgJGol4","pdf",7058399,1,13,"English","en",105,"# Introduction\n## Background and motivation\n## Development of design models for FRP-confined columns\n## Existing guideline- and model-based approaches","[{\"question\":\"What is the main objective of this research on FRP-confined columns?\",\"answer\":\"To evaluate the compressive strength of circular FRP-confined concrete columns using machine learning models and to improve prediction understanding through model comparison and parameter analysis.\"},{\"question\":\"How large is the database used for model development, and what types of materials does it include?\",\"answer\":\"The database contains 366 samples and includes both CFRP and GFRP-confined columns.\"},{\"question\":\"Which machine learning approach performed best among the considered models?\",\"answer\":\"The XGBoost model showed the highest goodness of fit (0.97) and the lowest coefficient of variation (8%), indicating strong predictive suitability.\"},{\"question\":\"Which parameters were found to significantly influence compressive strength?\",\"answer\":\"FRP thickness, elastic modulus, and concrete strength are highlighted as key parameters affecting compressive strength.\"}]","Machine learning-based prediction of compressive strength in circular FRP-confined concrete columns - Original Research | PDF",1785820707,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-based-prediction-of-compressive-strength-in-circular-frp-confined-concrete-columns-original-research","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-prediction-of-compressive-strength-in-circular-frp-confined-concrete-columns-original-research/124147/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this research on FRP-confined columns?","Question",{"text":75,"@type":76},"To evaluate the compressive strength of circular FRP-confined concrete columns using machine learning models and to improve prediction understanding through model comparison and parameter analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How large is the database used for model development, and what types of materials does it include?",{"text":80,"@type":76},"The database contains 366 samples and includes both CFRP and GFRP-confined columns.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performed best among the considered models?",{"text":84,"@type":76},"The XGBoost model showed the highest goodness of fit (0.97) and the lowest coefficient of variation (8%), indicating strong predictive suitability.",{"name":86,"@type":73,"acceptedAnswer":87},"Which parameters were found to significantly influence compressive strength?",{"text":88,"@type":76},"FRP thickness, elastic modulus, and concrete strength are highlighted as key parameters affecting compressive strength.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]