Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression
The paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-near...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/3766214 |
| _version_ | 1797784969750249472 |
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| author | J. Rajprasad P. Priya Rachel S. Arulselvan D. Arul G. Ramesh Kumar H. J. Pallavi M. Sivaraja Vinay Kumar Singh Getachew Gebreamlak |
| author_facet | J. Rajprasad P. Priya Rachel S. Arulselvan D. Arul G. Ramesh Kumar H. J. Pallavi M. Sivaraja Vinay Kumar Singh Getachew Gebreamlak |
| author_sort | J. Rajprasad |
| collection | DOAJ |
| description | The paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-nearest neighbor) method to combine them to obtain the layer regression vector (LRV), and it is combined with the reduced feature vector to obtain the improved LRV, then the output of this layer is taken; second, the regression vector (RV) of the input layer enhancement layer is used as input to obtain the output of the second layer FM, and the steps are repeated until the output of the input layer FM is complete. Finally, the output of the FM of the first layer is obtained. Several SFMs are trained and the result is obtained. The final prognosis is obtained by arithmetically averaging the forecast results of the SFMs of this layer. |
| first_indexed | 2024-03-13T00:47:28Z |
| format | Article |
| id | doaj.art-71ceb3e27bd9473eac48c9fbd29199b2 |
| institution | Directory Open Access Journal |
| issn | 1687-8442 |
| language | English |
| last_indexed | 2024-03-13T00:47:28Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj.art-71ceb3e27bd9473eac48c9fbd29199b22023-07-09T00:00:01ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84422023-01-01202310.1155/2023/3766214Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest RegressionJ. Rajprasad0P. Priya Rachel1S. Arulselvan2D. Arul3G. Ramesh Kumar4H. J. Pallavi5M. Sivaraja6Vinay Kumar Singh7Getachew Gebreamlak8Department of Civil EngineeringDepartment of Structural EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringCivil Engineering DepartmentDepartment of Mechanical EngineeringThe paper proposes a deep hybrid forest regression-based modeling method for measuring the compressive strength (CS) of concrete. Then, the reduced feature vector is used as input to train multiple subforest models (SFM), the predicted values are selected from multiple subforests via the KNN (K-nearest neighbor) method to combine them to obtain the layer regression vector (LRV), and it is combined with the reduced feature vector to obtain the improved LRV, then the output of this layer is taken; second, the regression vector (RV) of the input layer enhancement layer is used as input to obtain the output of the second layer FM, and the steps are repeated until the output of the input layer FM is complete. Finally, the output of the FM of the first layer is obtained. Several SFMs are trained and the result is obtained. The final prognosis is obtained by arithmetically averaging the forecast results of the SFMs of this layer.http://dx.doi.org/10.1155/2023/3766214 |
| spellingShingle | J. Rajprasad P. Priya Rachel S. Arulselvan D. Arul G. Ramesh Kumar H. J. Pallavi M. Sivaraja Vinay Kumar Singh Getachew Gebreamlak Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression Advances in Materials Science and Engineering |
| title | Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression |
| title_full | Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression |
| title_fullStr | Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression |
| title_full_unstemmed | Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression |
| title_short | Measurement of the Compressive Strength of Concrete Using Modeling of Deep Hybrid Forest Regression |
| title_sort | measurement of the compressive strength of concrete using modeling of deep hybrid forest regression |
| url | http://dx.doi.org/10.1155/2023/3766214 |
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