[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125107-en":3,"doc-seo-125107-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125107,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms - Abstract","Prediction of splitting tensile strength (STS) is essential for evaluating the structural performance of recycled aggregate concrete and for supporting sustainable construction decisions. The study addresses limitations of traditional laboratory testing, which is costly and time-consuming for large-scale use, by developing machine learning-based forecasting models. Using 257 measurements from prior work, models are built with support vector regression, XGBoost, and random forest, then assessed with MAE, RMSE, MAPE, and MASE. Random forest achieves the best accuracy with RMSE 1.76, improving prediction reliability for incorporating recycled materials.","University for Business and Technology in Kosovo  \nUBT Knowledge Center  \n\n| UBT International Conference | 2023 UBT International Conference |\n| --- | --- |\n| Oct 28th, 8:00 AM-Oct 29th, 6:00 PM\u003Cbr>Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms\u003Cbr>Lukesh Veloso de Parida\u003Cbr>Department of Civil Engineering, Shiv Nadar Institution of Eminence, Uttar Pradesh, Dadri, India, [Leonardo.kuhn@estudante.ufla.br](Leonardo.kuhn@estudante.ufla.br)\u003Cbr>Sumedha Melo de Moharana\u003Cbr>Federal University of Lavras, fabricio.fontenelle@estudante. ufla. br\u003Cbr>Sourav Kumar Giri\u003Cbr>Federal University of Lavras, [tulio.guimaraes2@estudante.ufla.br](tulio.guimaraes2@estudante.ufla.br)\u003Cbr>Follow this and additional works at: [https://knowledgecenter.ubt-uni.net/conference](https://knowledgecenter.ubt-uni.net/conference)\u003Cbr> Part of the Engineering Commons |  |\n\nRecommended Citation  \nParida, Lukesh Veloso de; Moharana, Sumedha Melo de; and Giri, Sourav Kumar, \"Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms\" (2023) . UBT International Conference. 35.  \n[https://knowledgecenter.ubt-uni.net/conference/IC/civil/35](https://knowledgecenter.ubt-uni.net/conference/IC/civil/35)  \nThis Event is brought to you for free and open access by the Publication and Journals at UBT Knowledge Center. It has been accepted for inclusion in UBT International Conference by an authorized administrator of UBT Knowledge Center. For more information, please contact [knowledge.center@ubt-uni.net](knowledge.center@ubt-uni.net).  \nSplitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms  \nLukesh Parida 1, Sumedha Moharana 1, Sourav Kumar Giri2  \n1Department of Civil Engineering, Shiv Nadar Institution of Eminence, Uttar Pradesh, Dadri, India, 201314  \n2School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar,  \nPatia, India,756001  \n[Presenting Author/Corresponding Author:lp617@snu.edu.in](Presenting Author/Corresponding Author:lp617@snu.edu.in)  \nAbstract. The prediction of splitting tensile strength, a crucial mechanical attribute determining structural performance, is an integral part of assessing the feasibility of recycled aggregates for construction. Traditional techniques for evaluating the splitting tensile strength of recycled aggregates rely on advanced and time-consuming laboratory testing, which may be costly and inefficient for large scale applications. This work proposes machine learning-based algorithms for forecasting the performance of splitting tensile strength. In this research, 257 measurements were acquired from a previous study containing input variables affecting split tensile strength. Three methods were used to build different predictive models, i.e., support vector regression, XG boost, and random forest. The performance indices of various models were evaluated using metrics like MAE, RMSE, MAPE, and MASE to measure the models' accuracy and reliability. This research indicates that Random forest algorithms outperform other models with RMSE value of 1.76. The implementation of proposed models improves the reliability of predictions, allowing researchers to make informed decisions about incorporating recycled materials in sustainable construction practices, thereby contributing to the reduction of environmental impacts in the construction sector.  \nKeywords: Recycled Aggregate Concrete (RAC), Machine Learning, Random Forest, XG Boost, Splitting Tensile Strength, Support Vector Regression  \n1 Introduction  \nConcrete is one of the world's most commonly employed construction materials due to its reliability, strength, and flexibility. In recent years, the construction sector has been challenged with an increasing demand for concrete, driven by industrialization and infrastructural expansion [1-3] . This spike in demand has resulted in an overconsumption of natural resources, especially aggregates, which are a critical compone","cbCaiqLPzrUx1wzK","https://ap.wps.com/l/cbCaiqLPzrUx1wzK","pdf",324948,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is splitting tensile strength prediction important in recycled aggregate concrete?\",\"answer\":\"STS is a key mechanical property used to judge the structural performance and durability of concrete, which is crucial when using recycled aggregates in construction.\"},{\"question\":\"What machine learning methods were used to build the predictive models?\",\"answer\":\"The research developed models using support vector regression (SVR), XGBoost, and random forest to forecast splitting tensile strength.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"Random forest outperformed the others, achieving an RMSE of 1.76. Models were evaluated using metrics including MAE, RMSE, MAPE, and MASE to measure accuracy and reliability.\"}]","Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms - Abstract | PDF",1785896687,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"splitting-tensile-strength-prediction-using-machine-learning-based-optimization-algorithms-abstract","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/splitting-tensile-strength-prediction-using-machine-learning-based-optimization-algorithms-abstract/125107/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is splitting tensile strength prediction important in recycled aggregate concrete?","Question",{"text":74,"@type":75},"STS is a key mechanical property used to judge the structural performance and durability of concrete, which is crucial when using recycled aggregates in construction.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What machine learning methods were used to build the predictive models?",{"text":79,"@type":75},"The research developed models using support vector regression (SVR), XGBoost, and random forest to forecast splitting tensile strength.",{"name":81,"@type":72,"acceptedAnswer":82},"Which model performed best and how was it evaluated?",{"text":83,"@type":75},"Random forest outperformed the others, achieving an RMSE of 1.76. 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