Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models
To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6629466 |
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| author | Hongxia Ma Jiandong Liu Jia Zhang Jiandong Huang |
| author_facet | Hongxia Ma Jiandong Liu Jia Zhang Jiandong Huang |
| author_sort | Hongxia Ma |
| collection | DOAJ |
| description | To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water-solid ratio of mining waste and fine sand ratio. |
| format | Article |
| id | doaj-art-b6007a734ed643f09248cdc48598913a |
| institution | DOAJ |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b6007a734ed643f09248cdc48598913a2025-08-20T03:20:55ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66294666629466Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest ModelsHongxia Ma0Jiandong Liu1Jia Zhang2Jiandong Huang3Jiangsu Province Xuzhou Technician Institute, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaTo estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water-solid ratio of mining waste and fine sand ratio.http://dx.doi.org/10.1155/2021/6629466 |
| spellingShingle | Hongxia Ma Jiandong Liu Jia Zhang Jiandong Huang Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models Advances in Civil Engineering |
| title | Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models |
| title_full | Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models |
| title_fullStr | Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models |
| title_full_unstemmed | Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models |
| title_short | Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models |
| title_sort | estimating the compressive strength of cement based materials with mining waste using support vector machine decision tree and random forest models |
| url | http://dx.doi.org/10.1155/2021/6629466 |
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