Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete
Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete h...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Journal of King Saud University: Engineering Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363921001239 |
| _version_ | 1797271378848645120 |
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| author | Sourav Ray Mohaiminul Haque Md. Masnun Rahman Md. Nazmus Sakib Kazi Al Rakib |
| author_facet | Sourav Ray Mohaiminul Haque Md. Masnun Rahman Md. Nazmus Sakib Kazi Al Rakib |
| author_sort | Sourav Ray |
| collection | DOAJ |
| description | Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content. |
| first_indexed | 2024-03-07T14:01:53Z |
| format | Article |
| id | doaj.art-36b0b6ebdfab41a480556cc7d660b5e8 |
| institution | Directory Open Access Journal |
| issn | 1018-3639 |
| language | English |
| last_indexed | 2024-03-07T14:01:53Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj.art-36b0b6ebdfab41a480556cc7d660b5e82024-03-07T05:26:54ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392024-02-01362112121Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concreteSourav Ray0Mohaiminul Haque1Md. Masnun Rahman2Md. Nazmus Sakib3Kazi Al Rakib4Corresponding author at: Department of Civil and Environmental Engineering, Room No: 108, Academic Building-‘C’, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.; Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDue to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content.http://www.sciencedirect.com/science/article/pii/S1018363921001239Ceramic waste aggregateCeramic waste aggregate concreteCompressive strengthSplitting Tensile strengthStrength predictionSupport vector machine |
| spellingShingle | Sourav Ray Mohaiminul Haque Md. Masnun Rahman Md. Nazmus Sakib Kazi Al Rakib Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete Journal of King Saud University: Engineering Sciences Ceramic waste aggregate Ceramic waste aggregate concrete Compressive strength Splitting Tensile strength Strength prediction Support vector machine |
| title | Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| title_full | Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| title_fullStr | Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| title_full_unstemmed | Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| title_short | Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| title_sort | experimental investigation and svm based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete |
| topic | Ceramic waste aggregate Ceramic waste aggregate concrete Compressive strength Splitting Tensile strength Strength prediction Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S1018363921001239 |
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