Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses
Abstract The construction sector is proactively working to minimize the environmental impact of cement manufacturing by adopting alternative cementitious substances and cutting carbon emissions tied to concrete. This study investigates the viability of using waste industrial materials as a replaceme...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-11601-x |
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| author | Md. Alhaz Uddin Md. Habibur Rahman Sobuz Md. Kawsarul Islam Kabbo Md. Kanan Chowdhury Tilak Ratan Lal Md. Selim Reza Fahad Alsharari Mohamed AbdelMongy Masuk Abdullah |
| author_facet | Md. Alhaz Uddin Md. Habibur Rahman Sobuz Md. Kawsarul Islam Kabbo Md. Kanan Chowdhury Tilak Ratan Lal Md. Selim Reza Fahad Alsharari Mohamed AbdelMongy Masuk Abdullah |
| author_sort | Md. Alhaz Uddin |
| collection | DOAJ |
| description | Abstract The construction sector is proactively working to minimize the environmental impact of cement manufacturing by adopting alternative cementitious substances and cutting carbon emissions tied to concrete. This study investigates the viability of using waste industrial materials as a replacement of cement in concrete mixes. The primary goal is to predict the compressive strength of waste-incorporated concrete by evaluating the effects of materials such as cement, fly ash (FA), silica fume (SF), ground granulated blast furnace slag (GGBFS), metakaolin (MK), water usage, aggregate levels, and superplasticizer dosages. A total of 441 data entries were sourced from various publications. Multiple machine learning techniques, such as light gradient boosting (LGB), extreme gradient boosting (XGB), and decision trees (DT), along with hybrid approaches like XGB-LGB and XGB-DT, were utilized to study how these variables influence compressive strength. The dataset was partitioned into training and testing, and statistical tools were employed to assess the correlation between input variables and strength. Model accuracy was gauged using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R2). Among the models, the XGB and DT approach delivered the highest precision, with an R2 of 0.928 in the training stage. Among hybrid models, XGB-DT exhibited a balanced performance having R2 value of 0.907 and 0.785 for training and testing phase. Additionally, SHAP (SHapley Additive exPlanations) and partial dependence plots (PDP) were employed to pinpoint the optimal ranges for each variable’s contribution to the improvement of compressive strength. SHAP and PDP analyses identified coarse aggregate, superplasticizers, water and cement content have high influence on model’s output. Additionally, 150–200 kg/m3 of GGBFS as key factors for optimizing compressive strength. The study concludes that the hybrid models along with the single models, can effectively forecast the compressive strength of concrete incorporating industrial byproducts, assisting the construction industry in efficiently evaluating material properties and understanding the influence of various input factors. |
| format | Article |
| id | doaj-art-dc534f1ab2b24d9c9becc35544801b36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-dc534f1ab2b24d9c9becc35544801b362025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-11601-xPredicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analysesMd. Alhaz Uddin0Md. Habibur Rahman Sobuz1Md. Kawsarul Islam Kabbo2Md. Kanan Chowdhury Tilak3Ratan Lal4Md. Selim Reza5Fahad Alsharari6Mohamed AbdelMongy7Masuk Abdullah8Department of Civil Engineering, College of Engineering, Jouf UniversityDepartment of Building Engineering and Construction Management, Khulna University of Engineering & TechnologyDepartment of Building Engineering and Construction Management, Khulna University of Engineering & TechnologyDepartment of Building Engineering and Construction Management, Khulna University of Engineering & TechnologyHJ Russell & CompanyDepartment of Software Engineering, Faculty of Science and Information Technology, Daffodil Smart City (DSC) Birulia, Daffodil International UniversityDepartment of Civil Engineering, College of Engineering, Jouf UniversityDepartment of Civil Engineering, College of Engineering, Jouf UniversityDepartment of Vehicles Engineering, University of DebrecenAbstract The construction sector is proactively working to minimize the environmental impact of cement manufacturing by adopting alternative cementitious substances and cutting carbon emissions tied to concrete. This study investigates the viability of using waste industrial materials as a replacement of cement in concrete mixes. The primary goal is to predict the compressive strength of waste-incorporated concrete by evaluating the effects of materials such as cement, fly ash (FA), silica fume (SF), ground granulated blast furnace slag (GGBFS), metakaolin (MK), water usage, aggregate levels, and superplasticizer dosages. A total of 441 data entries were sourced from various publications. Multiple machine learning techniques, such as light gradient boosting (LGB), extreme gradient boosting (XGB), and decision trees (DT), along with hybrid approaches like XGB-LGB and XGB-DT, were utilized to study how these variables influence compressive strength. The dataset was partitioned into training and testing, and statistical tools were employed to assess the correlation between input variables and strength. Model accuracy was gauged using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R2). Among the models, the XGB and DT approach delivered the highest precision, with an R2 of 0.928 in the training stage. Among hybrid models, XGB-DT exhibited a balanced performance having R2 value of 0.907 and 0.785 for training and testing phase. Additionally, SHAP (SHapley Additive exPlanations) and partial dependence plots (PDP) were employed to pinpoint the optimal ranges for each variable’s contribution to the improvement of compressive strength. SHAP and PDP analyses identified coarse aggregate, superplasticizers, water and cement content have high influence on model’s output. Additionally, 150–200 kg/m3 of GGBFS as key factors for optimizing compressive strength. The study concludes that the hybrid models along with the single models, can effectively forecast the compressive strength of concrete incorporating industrial byproducts, assisting the construction industry in efficiently evaluating material properties and understanding the influence of various input factors.https://doi.org/10.1038/s41598-025-11601-xSustainable concreteHybrid machine learningCompressive strengthIndustrial wasteParametric analysis. |
| spellingShingle | Md. Alhaz Uddin Md. Habibur Rahman Sobuz Md. Kawsarul Islam Kabbo Md. Kanan Chowdhury Tilak Ratan Lal Md. Selim Reza Fahad Alsharari Mohamed AbdelMongy Masuk Abdullah Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses Scientific Reports Sustainable concrete Hybrid machine learning Compressive strength Industrial waste Parametric analysis. |
| title | Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| title_full | Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| title_fullStr | Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| title_full_unstemmed | Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| title_short | Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| title_sort | predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses |
| topic | Sustainable concrete Hybrid machine learning Compressive strength Industrial waste Parametric analysis. |
| url | https://doi.org/10.1038/s41598-025-11601-x |
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