Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber
Waste management has become a new challenge for the construction industries since rapid urbanization is taking place worldwide. Ceramic waste is one such material which is being originated from construction sites and industries, imposing a significant risk to the environment due to its non-biodegrad...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Journal of King Saud University: Engineering Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363921000325 |
| _version_ | 1797868312988745728 |
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| author | Sourav Ray Md Masnun Rahman Mohaiminul Haque M. Washif Hasan M. Manjurul Alam |
| author_facet | Sourav Ray Md Masnun Rahman Mohaiminul Haque M. Washif Hasan M. Manjurul Alam |
| author_sort | Sourav Ray |
| collection | DOAJ |
| description | Waste management has become a new challenge for the construction industries since rapid urbanization is taking place worldwide. Ceramic waste is one such material which is being originated from construction sites and industries, imposing a significant risk to the environment due to its non-biodegradable nature. With the goal of waste utilization, this study aims to predict the compressive and splitting tensile strength of concrete made with waste Coarse Ceramic aggregate (CCA) and Nylon Fiber (NF) by using two distinct machine learning algorithms, namely, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). A comprehensive data set for testing and training the models containing 162 records of compressive and splitting tensile strength test results were considered from nine mix proportions. For training the dataset, parameters like cement content, sand content, stone content, ceramic content, nylon fiber content, curing duration, and concrete strength were taken as input variables. The predicted strengths obtained from the SVM and GBM based models are found to be in close agreement with the experimental results. In terms of coefficient of determination (R2), GBM showed significantly better result for both compressive strength (e.g., SVM Overall R2 = 0.879 & GBM Overall R2 = 0.981) and tensile strength (e.g., SVM Overall R2 = 0.706 & GBM Overall R2 = 0.923) prediction. Furthermore, based on the statistical accuracy measures like the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), it has been observed that GBM has yielded much better performance compared to SVM in predicting the mechanical strength of concrete. |
| first_indexed | 2024-04-09T23:55:00Z |
| format | Article |
| id | doaj.art-bc413a46f16042e998e330234cb660ec |
| institution | Directory Open Access Journal |
| issn | 1018-3639 |
| language | English |
| last_indexed | 2024-04-09T23:55:00Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj.art-bc413a46f16042e998e330234cb660ec2023-03-17T04:32:38ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392023-02-0135292100Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiberSourav Ray0Md Masnun Rahman1Mohaiminul Haque2M. Washif Hasan3M. Manjurul Alam4Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh; Corresponding 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 Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, United StatesWaste management has become a new challenge for the construction industries since rapid urbanization is taking place worldwide. Ceramic waste is one such material which is being originated from construction sites and industries, imposing a significant risk to the environment due to its non-biodegradable nature. With the goal of waste utilization, this study aims to predict the compressive and splitting tensile strength of concrete made with waste Coarse Ceramic aggregate (CCA) and Nylon Fiber (NF) by using two distinct machine learning algorithms, namely, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). A comprehensive data set for testing and training the models containing 162 records of compressive and splitting tensile strength test results were considered from nine mix proportions. For training the dataset, parameters like cement content, sand content, stone content, ceramic content, nylon fiber content, curing duration, and concrete strength were taken as input variables. The predicted strengths obtained from the SVM and GBM based models are found to be in close agreement with the experimental results. In terms of coefficient of determination (R2), GBM showed significantly better result for both compressive strength (e.g., SVM Overall R2 = 0.879 & GBM Overall R2 = 0.981) and tensile strength (e.g., SVM Overall R2 = 0.706 & GBM Overall R2 = 0.923) prediction. Furthermore, based on the statistical accuracy measures like the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), it has been observed that GBM has yielded much better performance compared to SVM in predicting the mechanical strength of concrete.http://www.sciencedirect.com/science/article/pii/S1018363921000325Support vector machineGradient boosting machineCeramic aggregateNylon fiberCompressive strengthSplitting tensile strength |
| spellingShingle | Sourav Ray Md Masnun Rahman Mohaiminul Haque M. Washif Hasan M. Manjurul Alam Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber Journal of King Saud University: Engineering Sciences Support vector machine Gradient boosting machine Ceramic aggregate Nylon fiber Compressive strength Splitting tensile strength |
| title | Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| title_full | Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| title_fullStr | Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| title_full_unstemmed | Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| title_short | Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| title_sort | performance evaluation of svm and gbm in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber |
| topic | Support vector machine Gradient boosting machine Ceramic aggregate Nylon fiber Compressive strength Splitting tensile strength |
| url | http://www.sciencedirect.com/science/article/pii/S1018363921000325 |
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