[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126395-en":3,"doc-seo-126395-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126395,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis","Study focuses on predicting compressive strength of waste marble dust concrete by combining machine learning and experimental verification. A dataset compiled from prior literature uses cement, fine and coarse aggregates, superplasticizers, silica fume, marble dust, and water as inputs, with compressive strength as the output. XG Boost provides the strongest predictive accuracy (R2 0.999 train, 0.915 test), while decision tree maintains R2 above 0.85. Experiments confirm strength gains for marble dust binder replacement of 5–20% by weight. Feature importance and PDP analysis show superplasticizers, cement, and silica fume dominate strength; microstructure observations support denser matrix and improved bonding.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis  \nAbdullah Alzlfawi1􀀍, Md. Habibur Rahman Sobuz2􀀍, Md. Kawsarul1, Islam Kabbo2, Mita Khatun2, Sani Aliyu Abubakar3􀀍, M. Jameel4 & Md Jihad Miah5  \nThe production of waste marble is the ultimate concern of the construction industry’s recent success in satisfying the huge need for marble stones. This study predicts the compressive strength of waste marble powder concrete using machine learning methods XG Boost, AdaBoost, Cat-Boost, GradientBoosting, Light Gradient-Boosting, and decision tree. For this purpose, a comprehensive dataset was developed from previously published literature, incorporating input features such as cement, fine and coarse aggregates, superplasticizers, silica fume, marble dust, and water, with compressive strength (CS) as the output parameter. Based on the outcomes, XG Boost model outperforms other models to predict CS with R2 values of 0.999 and 0.915 on the train and test stage, respectively. The decision tree model also shows consistent performance with R2 > 0.85 for both train and testing phases. In addition, experimental assessment was also conducted to verify the outcomes of machine learning (ML) modeling and quantify the CS of concrete specimens having marble dust as binder replacement (5–20% by weight) . In addition, feature importance analysis highlights that superplasticizers, cement, and silica fume were the most weighted factors that affected CS. According to partial dependency plot (PDP) analysis, combined data on concrete strength versus cement concentration shows that the strength of the material increases from 37.5 MPa to 57.5 MPa when the cement concentration increases from 300 kg/m3 to 500 kg/m3. Furthermore, microstructural evaluation revealed a dense and well-compacted matrix in concrete containing waste marble powder, with improved bonding between the binder and aggregates.  \nKeywords Machine learning, Compressive strength, Waste marble dust, Strength prediction, Parametric modeling.  \nConcrete has emerged as the most preferred construction material for various infrastructures ofthe twenty-first century on account of its long service life, durability, ease of preparation and fabrication from easily available constituents1–3. To date, concrete is produced more than 10 billion tons every year, constituting almost as much as 1.5 tons per capita worldwide4,5. It takes 0.8 to 1.2 cubic meters of aggregates to produce one cubic meter of concrete. Aggregates occupy 70–80% of the total volume of concrete, of which 25–30% is occupied by fine aggregate6. The fine aggregate is a natural resource, and its excessive demand has led to a shortage of resources. On the contrary, waste generation is rising due to high production, construction and demolition activities in developing countries and has become a matter of grave concern7,8. Currently, this waste is being recycled either  \n1Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Al Majmaah 11952, Saudi Arabia. 2Department of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh. 3Department of Civil Engineering, Kampala International University, Western Campus, Ishaka –Bushenyi, Western Region, Kampala, Uganda. 4Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia. 5Department of Architecture, Technology and Engineering, University of Brighton, Lewes Road, Brighton BN2 4GJ, UK. 􀀍 email: [a.alzlfawi@mu.edu.sa](a.alzlfawi@mu.edu.sa); [habib@becm.kuet.ac.bd](habib@becm.kuet.ac.bd); [saliyu@kiu.ac.ug](saliyu@kiu.ac.ug)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nin the form of landfilling or disposed of9–12. This waste releases huge amounts of fine","cbCairg6wFrk2cIK","https://ap.wps.com/l/cbCairg6wFrk2cIK","pdf",6989952,7,1,26,"English","en",105,"# Prediction and parametric modeling\n## Machine learning models for compressive strength\n## Dataset and input features\n## Model performance and validation\n## Experimental assessment with marble dust replacement\n## Feature importance and PDP analysis\n## Microstructural evaluation","[{\"question\":\"Which machine learning models are used to predict compressive strength?\",\"answer\":\"The study applies XG Boost, AdaBoost, Cat-Boost, GradientBoosting, Light Gradient-Boosting, and decision tree models to predict compressive strength.\"},{\"question\":\"How is the dataset for modeling constructed and what are the main variables?\",\"answer\":\"A comprehensive dataset is developed from previously published literature, using cement, aggregates, superplasticizers, silica fume, marble dust, and water as input features, with compressive strength as the output.\"},{\"question\":\"How does the experimental binder replacement level of marble dust affect compressive strength?\",\"answer\":\"Concrete specimens with marble dust as binder replacement from 5–20% by weight are experimentally evaluated to verify the machine learning predictions and quantify compressive strength changes.\"}]","Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis | PDF",1785904824,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"prediction-and-parametric-modeling-of-compressive-strength-of-waste-marble-dust-concrete-through-machine-learning-and-experimental-analysis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/prediction-and-parametric-modeling-of-compressive-strength-of-waste-marble-dust-concrete-through-machine-learning-and-experimental-analysis/126395/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning models are used to predict compressive strength?","Question",{"text":77,"@type":78},"The study applies XG Boost, AdaBoost, Cat-Boost, GradientBoosting, Light Gradient-Boosting, and decision tree models to predict compressive strength.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the dataset for modeling constructed and what are the main variables?",{"text":82,"@type":78},"A comprehensive dataset is developed from previously published literature, using cement, aggregates, superplasticizers, silica fume, marble dust, and water as input features, with compressive strength as the output.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the experimental binder replacement level of marble dust affect compressive strength?",{"text":86,"@type":78},"Concrete specimens with marble dust as binder replacement from 5–20% by weight are experimentally evaluated to verify the machine learning predictions and quantify compressive strength 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