[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121329-en":3,"doc-seo-121329-105":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},121329,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Analysis in Materials Science by Predicting Concrete Compressive Strength Using Machine Learning","Machine learning is applied to predict concrete compressive strength based on eight independent input variables: cement, blast furnace slag, fly ash, water, super plasticizers, coarse aggregate, fine aggregate, and age. Supervised models are trained and tested with 1030 datasets using a 70:30 split, employing linear regression (LR) and light gradient boosting machine (LGBM). Model quality is evaluated using R² and RMSE metrics. Results show LGBM outperforms LR (R² 0.920 vs 0.607), and LGBM feature importance highlights cement, fine aggregate, and coarse aggregate as major drivers.","Research Article  \nAnalysis in Materials Science by Predicting Concrete Compressive Strength  \nUsing Machine Learning  \n* **  \nT. Hakimi , M. Bhuyan  \nCenter for Theoretical and Computational Physics, Department of Physics, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia.  \n* [Email:](Email:taufiq_hakimi@yahoo.com)[taufiq_hakimi@yahoo.com](Email:taufiq_hakimi@yahoo.com) (corresponding author)  \n** ORCID:0000-0002-8677-4220 (MBhuyan)  \n(Received: 26-02-24; Accepted:12-05-24)  \n\n| ABSTRACT: Future developments in materials science engineering will be greatly influenced by the application of machine learning for determining the properties of concrete, especially its compressive strength. This research predicts the compressive strength of concrete with eight independent variables, including cement, blast furnace slag, fly ash, water, super plasticizers, coarse aggregate, fine aggregate, and age using supervised machine learning (ML) techniques of linear regression (LR) and light gradient boosting machine (LGBM) . The ML models are fed a total of 1030 data-sets using a 70 :30 split ratio for training and testing. Performance metrics like 􀀧2, 􀀢 􀀖􀀚, 􀀢􀀨􀀚, and RMSE are used to assess how well the ML models are in making predictions. From the research, the LR model (􀀧2 value of 0 .607) is less effective than the LGBM model (􀀧2 value of 0 .920) in predicting compressive strength. Furthermore, feature importance predicted by LGBM shows that the cement content (2331), fine aggregate (2200), and coarse aggregate (2076) all significantly influence the prediction of concrete compressive strength.\u003Cbr>Keywords: Material Science, Machine learning, Properties of concrete, Cement, Fly ash, super-plasticizers |  |\n| --- | --- |\n| 1. Introduction\u003Cbr>In the field of materials science, machine learning (ML) is widely used. ML is a branch of Artificial Intelligence (AI) that uses algorithms to self-learn and enhance its performance using past data-sets. ML algorithms will automatically learn and get better over time with very little human involvement [1] . AI and ML have already been used in engineering to overcome issues in various structural engineering domains [2] . Further applications of machine learning include the prediction and evaluation of concrete characteristics, the improvement of finite element modelling of buildings, and building structural design and performance assessment [3] . Compressive strength is the most important of the several concrete properties, as it is used to evaluate the performance of structures, from new structural design to old structural assessment [4] . Cement, water, | fine aggregate, and coarse aggregate are the four main ingredients of concrete [5] . To improve the quality of concrete, additional materials such as industrial wastes or by-products are occasionally added [6] . Each of the ingredients has its unique properties that contribute to the overall strength of the concrete. Cement has a significant impact on the most critical elements of a concrete mixture, including workability, compressive strength, drying shrinkage, and durability. Water starts the cement’s hydration process and gives the mixture workability. The ratio of water to cement is important because too little water can make the concrete difficult to work with and too much water can weaken it [7] . Fine aggregates are typically made up of natural sand or broken stone, with the majority of the particles going through a 3/8-inch screen. Strength |\n\n© 2024 Author(s). This article is published under the CC-BY license at [http://jpr.vyomhansjournals.com](http://jpr.vyomhansjournals.com).  \nFigure 1: Boxplot of nine variables  \nis increased because it fills up the voids between cement and coarse particles, providing better particle packing. Coarse aggregates are any particles larger than 0.19 inches, nevertheless, their typical diameter ranges from 3/8 to 1.5 inches. It gives concrete construction strength, thermal and elastic prope","cbCaia85JCyf4HsR","https://ap.wps.com/l/cbCaia85JCyf4HsR","pdf",1557551,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What variables are used to predict concrete compressive strength?\",\"answer\":\"The prediction uses cement, blast furnace slag, fly ash, water, super plasticizers, coarse aggregate, fine aggregate, and age.\"},{\"question\":\"Which machine learning models are compared in the research?\",\"answer\":\"The study compares linear regression (LR) with light gradient boosting machine (LGBM).\"},{\"question\":\"How does LGBM perform compared with LR?\",\"answer\":\"LGBM performs better, achieving an R² of 0.920 versus 0.607 for LR, indicating more accurate compressive strength predictions.\"}]","Analysis in Materials Science by Predicting Concrete Compressive Strength Using Machine Learning | PDF",1785735092,23,{"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},"analysis-in-materials-science-by-predicting-concrete-compressive-strength-using-machine-learning","",{"@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/analysis-in-materials-science-by-predicting-concrete-compressive-strength-using-machine-learning/121329/",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},"What variables are used to predict concrete compressive strength?","Question",{"text":75,"@type":76},"The prediction uses cement, blast furnace slag, fly ash, water, super plasticizers, coarse aggregate, fine aggregate, and age.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the research?",{"text":80,"@type":76},"The study compares linear regression (LR) with light gradient boosting machine (LGBM).",{"name":82,"@type":73,"acceptedAnswer":83},"How does LGBM perform compared with LR?",{"text":84,"@type":76},"LGBM performs better, achieving an R² of 0.920 versus 0.607 for LR, indicating more accurate compressive strength predictions.","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"]