[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121447-en":3,"doc-seo-121447-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121447,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Performance evaluation of compressive strength of concrete using different machine learning algorithms","Accurately predicting the compressive strength of concrete is crucial for ensuring structural integrity, optimizing material usage, and reducing construction costs. Conventional experimental methods, though reliable, are often labour-intensive and time-consuming. This study evaluates four machine learning algorithms—Linear Regression, Multilayer Perceptron, M5 rule-based model, and Support Vector Machines—to predict concrete compressive strength. A dataset of 350 concrete samples is tested using multiple train-test splits to measure prediction accuracy. Results show the MLP achieves the highest correlation coefficient (0.98) with a 20% testing split, and robustness is further supported through multiple linear regression validation. The findings highlight machine learning’s potential for accurate, reliable, and time-efficient decision-making in construction material design.","| Research Article\u003Cbr>Performance evaluation of compressive strength of concrete using different machine learning algorithms\u003Cbr>Supriya Siddharth Morea,* , Ajaykumar R. Kambekara \u003Cbr>a Department of Civil Engineering, Sardar Patel College of Engineering, Andheri West ,400058 Maharashtra, India |  |\n| --- | --- |\n| A B S T RA C T\u003Cbr>Accurately predicting the compressive strength of concrete is crucial for ensuring structural integrity, optimizing material usage, and reducing construction costs. Conventional experimental methods, though reliable, are often labour-intensive and time-consuming. To address these limitations, this study investigates the effectiveness of machine learning (ML) algorithms as efficient alternatives for predicting concrete compressive strength. Four ML algorithms—Linear Regression (LR), Multilayer Perceptron (MLP), M5 Rule-Based Model, and Support Vector Machines (SVM)—were evaluated based on their predictive performance. A comprehensive dataset comprising 350 concrete samples was prepared, with compressive strength tests conducted in accordance with Indian standard 516. The models were trained on experimental data and were tested using varying data splits of 50%, 40%, 30%, 20%, and 10% to assess their prediction accuracy. Among the evaluated models, the MLP demonstrated superior performance, achieving a correlation coefficient (CC) of 0.98 with a 20% testing split, outperforming the other algorithms. To further validate the predictive capability of the MLP model, multiple linear regression analysis was employed, confirming its robustness and generalization ability. The findings underscore the potential of machine learning techniques, particularly the MLP model, in providing accurate, reliable, and time-efficient predictions of concrete compressive strength. This study contributes to the growing body of research focused on leveraging machine learning for enhanced decision-making in construction material design, ultimately promoting more sustainable and cost-effective construction practices. | ARTICLE INFO\u003Cbr>Article history:\u003Cbr>Received ‒ January 20, 2025\u003Cbr>Revision requested‒ February 18, 2025 Revision received ‒ March 11, 2025\u003Cbr>Accepted ‒ March 22, 2025 Keywords:\u003Cbr>Linear regression\u003Cbr>Multilayer perceptron\u003Cbr>M5 rule-based model\u003Cbr>Support vector machines\u003Cbr>\u003Cbr>This is an open access article distributed under the CC BY licence.\u003Cbr>© 2025 by the Authors. |\n| Citation: More SS, Kambekar A (2025). Performance evaluation of compressive strength of concrete using different machine learning algorithms.\u003Cbr>Challenge Journal of Concrete Research Letters, 16(2), 60–68. |  |\n\n1. Introduction  \nThe field of construction materials research has undergone a significant transformation with the advent of machine learning (ML) techniques, offering novel approaches for analyzing and predicting the properties of materials. Machine learning enables the development of computational models capable of learning from data and identifying complex patterns, thereby offering potentially more efficient and effective solutions to structural engineering challenges (Gürbüz and Kazaz 2024) . Among these properties, compressive strength is a criti-  \ncal parameter that defines the quality, safety, and durability of concrete structures (Nalina 2023) . The application of advanced ML algorithms to predict the compressive strength of concrete can significantly accelerate labor-intensive experimental processes and reduce associated costs (Harirchian 2024) . Traditional methods for determining compressive strength often involve timeconsuming laboratory tests and physical experiments. As construction demands grow and evolve, there is a pressing need for efficient, accurate, and cost-effective methods to predict compressive strength during the design phase. While these methods ensure precision, they  \nare limited by high costs, delays, and constraints in realtime adaptability. In the modern era of construction and infrastructure demands,","cbCaicZvx1jcsUVq","https://ap.wps.com/l/cbCaicZvx1jcsUVq","pdf",1080064,1,9,"English","en",105,"# Introduction\n## Construction materials and ML-based prediction\n# Literature Review\n## Machine learning models for compressive strength prediction\n# Methodology\n## Dataset preparation and testing strategy\n# Results and Discussion\n## Predictive performance comparison\n# Model Validation\n## Robustness and generalization analysis","[{\"question\":\"Which machine learning algorithms are evaluated for predicting concrete compressive strength?\",\"answer\":\"The study evaluates Linear Regression, Multilayer Perceptron (MLP), an M5 rule-based model, and Support Vector Machines (SVM).\"},{\"question\":\"How was the dataset prepared and how were models tested?\",\"answer\":\"A dataset of 350 concrete samples was compiled, with compressive strength tests aligned with Indian standard 516. Models were trained on experimental data and tested using varying data splits of 50%, 40%, 30%, 20%, and 10%.\"},{\"question\":\"Which model performed best and under what testing split?\",\"answer\":\"The MLP demonstrated the best predictive performance, reaching a correlation coefficient of 0.98 with a 20% testing split.\"}]","Performance evaluation of compressive strength of concrete using different machine learning algorithms | PDF",1785735704,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"performance-evaluation-of-compressive-strength-of-concrete-using-different-machine-learning-algorithms","",{"@graph":36,"@context":85},[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/performance-evaluation-of-compressive-strength-of-concrete-using-different-machine-learning-algorithms/121447/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are evaluated for predicting concrete compressive strength?","Question",{"text":75,"@type":76},"The study evaluates Linear Regression, Multilayer Perceptron (MLP), an M5 rule-based model, and Support Vector Machines (SVM).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset prepared and how were models tested?",{"text":80,"@type":76},"A dataset of 350 concrete samples was compiled, with compressive strength tests aligned with Indian standard 516. Models were trained on experimental data and tested using varying data splits of 50%, 40%, 30%, 20%, and 10%.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and under what testing split?",{"text":84,"@type":76},"The MLP demonstrated the best predictive performance, reaching a correlation coefficient of 0.98 with a 20% testing split.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]