Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model
Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to...
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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/10.1155/2021/6671448 |
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| _version_ | 1832560015807873024 |
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| author | Hai-Van Thi Mai Thuy-Anh Nguyen Hai-Bang Ly Van Quan Tran |
| author_facet | Hai-Van Thi Mai Thuy-Anh Nguyen Hai-Bang Ly Van Quan Tran |
| author_sort | Hai-Van Thi Mai |
| collection | DOAJ |
| description | Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729, 4.9585, and 3.9423 for the testing dataset. The results show that the RF algorithm is an excellent predictor and practically used for engineers to reduce experimental cost. |
| format | Article |
| id | doaj-art-04c456828d3b4bdda95507df5a5d19c5 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-04c456828d3b4bdda95507df5a5d19c52025-02-03T01:28:32ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66714486671448Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest ModelHai-Van Thi Mai0Thuy-Anh Nguyen1Hai-Bang Ly2Van Quan Tran3University of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamImprovement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729, 4.9585, and 3.9423 for the testing dataset. The results show that the RF algorithm is an excellent predictor and practically used for engineers to reduce experimental cost.http://dx.doi.org/10.1155/2021/6671448 |
| spellingShingle | Hai-Van Thi Mai Thuy-Anh Nguyen Hai-Bang Ly Van Quan Tran Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model Advances in Civil Engineering |
| title | Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model |
| title_full | Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model |
| title_fullStr | Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model |
| title_full_unstemmed | Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model |
| title_short | Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model |
| title_sort | prediction compressive strength of concrete containing ggbfs using random forest model |
| url | http://dx.doi.org/10.1155/2021/6671448 |
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