Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach
Abstract The self-compacting concrete (SCC) mixes were developed using lightweight expandable clay aggregate (LECA) as a partial substitute for coarse aggregate, ground granulated blast-furnace slag (GGBS) as a partial replacement for cement, and combusted bio-medical waste ash (BMWA) as a partial r...
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Nature Portfolio
2025-04-01
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| author | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh Ahmed M. Ebid Janneth Alejandra Viñan Villagran Raúl Gregorio Martínez Pérez Fausto Ulpiano Caicedo Benavides Paul Awoyera Siva Avudaiappan |
| author_facet | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh Ahmed M. Ebid Janneth Alejandra Viñan Villagran Raúl Gregorio Martínez Pérez Fausto Ulpiano Caicedo Benavides Paul Awoyera Siva Avudaiappan |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Abstract The self-compacting concrete (SCC) mixes were developed using lightweight expandable clay aggregate (LECA) as a partial substitute for coarse aggregate, ground granulated blast-furnace slag (GGBS) as a partial replacement for cement, and combusted bio-medical waste ash (BMWA) as a partial replacement for fine aggregate. The substitution levels for LECA, GGBS, and BMWA were set at 10%, 20%, and 30% of coarse aggregate, cement, and fine aggregate, respectively. M30-grade SCC mixes were designed with two different water-to-binder ratios—0.40 and 0.45—and their compressive strength (CS) was experimentally evaluated. The data entries from the above mix designs and experiments were collected in this research which deals with evaluating the impact of lightweight expandable clay aggregate, metallurgical slag, and combusted bio-medical waste ash on self-compacting concrete. An extensive literature search was used in this project and this produced a global representative database collected from literature. The collected 384 records were divided into training set (300 records = 80%) and validation set (84 records = 20%) in line with the requirements of a more reliable data partitioning. Six advanced machine learning methods such as the Artificial Neural Network (ANN), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGB), Random Forest (RF), and Adaptive Boosting (AdaBoost) were used to model the concrete behavior. All models were created using “Orange Data Mining” software version 3.36. A combination of error metrics, efficiency metrics and determination/correlation metrics were used to test the models performance and accuracy. Also, the Hoffman and Gardener’s method was used to evaluate the sensitivity analysis of the model variables. At the end of the model work, AdaBoost and KNN excel in predictive accuracy with 97.5%, reducing the margin of error and ensuring precise mix designs for SCC. SVR, XGB, and RF also exhibit strong accuracy (96.5–97%), supporting reliable material selection and proportions. AdaBoost and KNN demonstrate the lowest errors (MAE: 0.65 MPa, RMSE: 0.75 MPa), indicating precise performance, minimizing overdesign or underperformance risks, and optimizing material usage. The Hoffman/Gardener’s sensitivity analysis produced produced GGBS of 31% and Dens of 26% as the highest impact and this is followed by LECA of 21% and BMWA of 20%. This research enables the optimization of self-compacting concrete mix designs using machine learning, reducing experimental trials, enhancing material efficiency, lowering environmental impact, and promoting sustainable construction through the effective reuse of industrial by-products. |
| format | Article |
| id | doaj-art-5d262f0072ee4bcb969d5ab073f44495 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-5d262f0072ee4bcb969d5ab073f444952025-08-20T02:19:07ZengNature PortfolioScientific Reports2045-23222025-04-0115113210.1038/s41598-025-99091-9Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approachKennedy C. Onyelowe0Viroon Kamchoom1Shadi Hanandeh2Ahmed M. Ebid3Janneth Alejandra Viñan Villagran4Raúl Gregorio Martínez Pérez5Fausto Ulpiano Caicedo Benavides6Paul Awoyera7Siva Avudaiappan8Department of Civil Engineering, Michael Okpara University of AgricultureExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang (KMITL)Department of Civil Engineering, Al-Balqa Applied UniversityDepartment of Civil Engineering, Faculty of Engineering, Future University in EgyptFacultad de Recursos Naturales, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Mecánica, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Mecánica, Escuela Superior Politécnica de Chimborazo (ESPOCH)Department of Civil Engineering, Prince Mohammad bin Fahd UniversityDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica MetropolitanaAbstract The self-compacting concrete (SCC) mixes were developed using lightweight expandable clay aggregate (LECA) as a partial substitute for coarse aggregate, ground granulated blast-furnace slag (GGBS) as a partial replacement for cement, and combusted bio-medical waste ash (BMWA) as a partial replacement for fine aggregate. The substitution levels for LECA, GGBS, and BMWA were set at 10%, 20%, and 30% of coarse aggregate, cement, and fine aggregate, respectively. M30-grade SCC mixes were designed with two different water-to-binder ratios—0.40 and 0.45—and their compressive strength (CS) was experimentally evaluated. The data entries from the above mix designs and experiments were collected in this research which deals with evaluating the impact of lightweight expandable clay aggregate, metallurgical slag, and combusted bio-medical waste ash on self-compacting concrete. An extensive literature search was used in this project and this produced a global representative database collected from literature. The collected 384 records were divided into training set (300 records = 80%) and validation set (84 records = 20%) in line with the requirements of a more reliable data partitioning. Six advanced machine learning methods such as the Artificial Neural Network (ANN), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGB), Random Forest (RF), and Adaptive Boosting (AdaBoost) were used to model the concrete behavior. All models were created using “Orange Data Mining” software version 3.36. A combination of error metrics, efficiency metrics and determination/correlation metrics were used to test the models performance and accuracy. Also, the Hoffman and Gardener’s method was used to evaluate the sensitivity analysis of the model variables. At the end of the model work, AdaBoost and KNN excel in predictive accuracy with 97.5%, reducing the margin of error and ensuring precise mix designs for SCC. SVR, XGB, and RF also exhibit strong accuracy (96.5–97%), supporting reliable material selection and proportions. AdaBoost and KNN demonstrate the lowest errors (MAE: 0.65 MPa, RMSE: 0.75 MPa), indicating precise performance, minimizing overdesign or underperformance risks, and optimizing material usage. The Hoffman/Gardener’s sensitivity analysis produced produced GGBS of 31% and Dens of 26% as the highest impact and this is followed by LECA of 21% and BMWA of 20%. This research enables the optimization of self-compacting concrete mix designs using machine learning, reducing experimental trials, enhancing material efficiency, lowering environmental impact, and promoting sustainable construction through the effective reuse of industrial by-products.https://doi.org/10.1038/s41598-025-99091-9Sustainable constructionLightweight expandable clay aggregateCombusted bio-medical waste ashSelf-compacting concreteCompressive strengthAdvanced machine learning |
| spellingShingle | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh Ahmed M. Ebid Janneth Alejandra Viñan Villagran Raúl Gregorio Martínez Pérez Fausto Ulpiano Caicedo Benavides Paul Awoyera Siva Avudaiappan Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach Scientific Reports Sustainable construction Lightweight expandable clay aggregate Combusted bio-medical waste ash Self-compacting concrete Compressive strength Advanced machine learning |
| title | Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach |
| title_full | Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach |
| title_fullStr | Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach |
| title_full_unstemmed | Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach |
| title_short | Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach |
| title_sort | impact of lightweight clay aggregate with slag and biomedical waste ash on self compacting concrete using machine learning approach |
| topic | Sustainable construction Lightweight expandable clay aggregate Combusted bio-medical waste ash Self-compacting concrete Compressive strength Advanced machine learning |
| url | https://doi.org/10.1038/s41598-025-99091-9 |
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