A Comparative Study on the Compressive Strength of Cement-Based Composites Using Machine Learning Models

A Comparative Study on the Compressive Strength of Cement-Based Composites Using Machine Learning Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Comparative Study on the Compressive Strength of Cement-Based Composites Using Machine Learning Models Wenyi Yang, Aftab Anwar, Yuanjun Jiang, Wania Naz, Wang Yanwei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4446089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This work aims to compare the compressive strength of CNFs reinforced concrete, cement paste, and cement mortar utilizing machine learning models for prediction before construction. To obtain this goal, the ten supervised regression ML models were executed. The datasets with an experimental foundation consisting of 266, 233, and 196 data points for cement paste, cement mortar, and concrete respectively were set and split into training and testing groups for the model’s execution. There were seven input parameters: cement, water, CNFs, superplasticizer, fine aggregate, coarse aggregate, and age, and one output parameter: compressive strength fc. The results declared that seven models for cement paste, six models for cement mortar, and eight models for concrete had a strong ability to predict compressive strength. According to the sensitivity analysis, water, and cement were the parameters with the largest impacts on predicting the CNFs reinforced cement-based composites, while coarse aggregate was the smallest. It can be concluded that the three XGBR, GBR, and RF models for concrete, three XGBR, DT, and GBR models for cement paste, and three KNN, BR, and RF models for cement mortar were the best prediction models. Machine learning compressive strength cement based composites sensitivity analysis Full Text Additional Declarations No competing interests reported. Supplementary Files CNFscementmortarexcel.xlsx CNFscementpasteexcel.xlsx CNFsconcreteexcel.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4446089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308116150,"identity":"2e5aa19d-8725-4bcd-8d56-4392026c1596","order_by":0,"name":"Wenyi Yang","email":"","orcid":"","institution":"Yunnan Agriculture University","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Yang","suffix":""},{"id":308116151,"identity":"312a2a80-3d79-4d1c-bfed-1555bed0280f","order_by":1,"name":"Aftab 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