[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121562-en":3,"doc-seo-121562-105":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},121562,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Efficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution - study summary","Study develops efficient machine learning models for predicting the compressive strength of zeolite- and diatomite-modified concrete exposed to sodium chloride solution. Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting are trained with tuned hyperparameters. Experiments use 28-, 56-, and 90-day observations from 63 samples across seven concrete mixtures. Prediction quality is evaluated using RMSE, MAPE, and R2, showing improved accuracy after tuning and highlighting Categorical Boosting’s best performance. ","Arabian Journal for Science and Engineering (2024) 49:14241–14256  \n[https://doi.org/10.1007/s13369-024-09042-1](https://doi.org/10.1007/s13369-024-09042-1)  \nEfficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution  \nGiyasettin Ozcan1 · Burak Kocak2 · Eyyup Gulbandilar3 · Yilmaz Kocak4  \nReceived: 19 December 2023 / Accepted: 28 March 2024 / Published online: 18 April 2024 © The Author(s) 2024  \nAbstract  \nThis study implements a set of machine learning algorithms to building material science, which predict the compressive strength of zeolite and diatomite substituting concrete mixes in sodium chloride solution. Particularly, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting algorithms are exploited and their optimal parameters are tuned. In the training and testing of these models, 28 day, 56 day, and 90 day compressive strength observations of 63 samples of 7 different concrete mixtures substituting Portland cement, zeolite, diatomite, zeolite + diatomite were used. Consequently, compressive strength experimentation results and machine learning predictions were compared through statistical methods such as RMSE, MAPE, and R2. Results denote that the prediction performance of machine learning is improving with tuned models. Particularly, RMSE, MAPE, R2 scores of Categorical Boosting are, respectively, 1.15, 1.45%, and 98.03% after parameter tuning design. The results denote that presented machine learning model can provide an advantage in the cost and duration of the compressive strength experiments.  \nKeywords Zeolite · Diatomite · Compressive strength · Random forest · Gradient boosting · Machine learning  \n1 Introduction  \nConventional concrete is generally not successful in preventing the ingress of moisture and aggressive ions due to its porous structure[1]. For this reason, the use of supplementary cementitious materials in concrete is becoming more common day by day [2–6] . Mineral additives such as zeolite and diatomite are among the important building materials used extensively in cement and concrete technology, due to their economic and ecological advantages as well as contributing to the improvement of properties such as durability and mechanical properties of concrete [7–11] . For this reason, it  \nB Giyasettin Ozcan [gozcan@uludag.edu.tr](gozcan@uludag.edu.tr)  \n1 Department of Computer Engineering, Faculty of Engineering, Bursa Uludag University, Bursa, Turkey  \n2 Department of Computer Engineering, Institute of Postgraduate Education, Duzce University, Duzce, Turkey  \n3 Department of Computer Engineering, Faculty of Engineering and Architecture, Osmangazi University, Eskisehir, Turkey  \n4 Department of Civil Engineering, Faculty of Engineering, Duzce University, Duzce, Turkey  \nis necessary to understand mechanical properties of concrete structures under external loads. In this context, compressive strength, which is directly concerned with the safety of structures, is one of the most basic mechanical properties, and necessary in determining the performance of structures. The compressive strength of concrete is affected by many factors such as cement type, mineral and chemical additives, mixing ratios, curing conditions, mixing, transportation, placement and testing methods ofconcrete[2, 4, 5, 12–14]. To determine the compressive strength value of concrete, cubic or cylindrical samples are produced according to certain mixing ratios, cured under suitable conditions and the results of the compressive strength are determined by breaking them at certain times. Nevertheless, these experiments are costly in terms of both time and economy. For this purpose, different from traditional experimental methods and some artificial intelligence methods are used to predict the compressive strength of concrete with mixing ratios in concrete design.  \nTo reduce time and econ","cbCaitwxoPV9guHI","https://ap.wps.com/l/cbCaitwxoPV9guHI","pdf",2007782,1,16,"English","en",105,"# Abstract\n# Introduction\n## Motivation and background\n## Factors affecting compressive strength\n## Limitations of experimental methods\n## Machine learning as an alternative\n## Ensemble learning and selected algorithms","[{\"question\":\"Which machine learning algorithms are used to predict compressive strength in this study?\",\"answer\":\"The study uses Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting, with tuned optimal parameters.\"},{\"question\":\"What data and curing/measurement times are used for model training and testing?\",\"answer\":\"Models are trained and tested using 28-day, 56-day, and 90-day compressive strength observations from 63 samples across seven concrete mixtures.\"},{\"question\":\"How is prediction performance evaluated and what is the best result reported?\",\"answer\":\"Performance is assessed using RMSE, MAPE, and R2. After parameter tuning, Categorical Boosting achieves RMSE 1.15, MAPE 1.45%, and R2 98.03%.\"}]","Efficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution - study summary | PDF",1785736255,40,{"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},"efficient-machine-learning-models-for-estimation-of-compressive-strengths-of-zeolite-and-diatomite-substituting-concrete-in-sodium-chloride-solution-study-summary","",{"@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/efficient-machine-learning-models-for-estimation-of-compressive-strengths-of-zeolite-and-diatomite-substituting-concrete-in-sodium-chloride-solution-study-summary/121562/",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 used to predict compressive strength in this study?","Question",{"text":75,"@type":76},"The study uses Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting, with tuned optimal parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and curing/measurement times are used for model training and testing?",{"text":80,"@type":76},"Models are trained and tested using 28-day, 56-day, and 90-day compressive strength observations from 63 samples across seven concrete mixtures.",{"name":82,"@type":73,"acceptedAnswer":83},"How is prediction performance evaluated and what is the best result reported?",{"text":84,"@type":76},"Performance is assessed using RMSE, MAPE, and R2. 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