Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model
This research proposes a stacking machine learning method to accurately predict the compressive strength of recycled concrete. The model integrates eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), and Linear Regression (LR) models, aiming to maximize the prediction accuracy...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10806804/ |
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| _version_ | 1841550797817511936 |
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| author | Binghao Pan Wensheng Liu Pan Zhou Dapeng Oliver Wu |
| author_facet | Binghao Pan Wensheng Liu Pan Zhou Dapeng Oliver Wu |
| author_sort | Binghao Pan |
| collection | DOAJ |
| description | This research proposes a stacking machine learning method to accurately predict the compressive strength of recycled concrete. The model integrates eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), and Linear Regression (LR) models, aiming to maximize the prediction accuracy of concrete compressive strength. The model was evaluated using a combination of 63 self-made recycled concrete datasets and 1030 concrete datasets from the UCI Machine Learning Repository. Through optimization based on SHAP values, the new model achieved statistical metrics of RMSE = 1.969, MAE = 1.113, and R2 = 0.987. The comparison and analysis with the existing work show that this method has excellent performance. Additionally, the feature importance analysis based on SHAP values identified the key input variables affecting concrete compressive strength and improved the model’s prediction performance. |
| format | Article |
| id | doaj-art-c4b387822c5649c59669d4b7dd85b0c3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c4b387822c5649c59669d4b7dd85b0c32025-01-10T00:02:53ZengIEEEIEEE Access2169-35362025-01-01132958296910.1109/ACCESS.2024.351966910806804Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning ModelBinghao Pan0https://orcid.org/0009-0008-4868-1323Wensheng Liu1Pan Zhou2https://orcid.org/0000-0002-8629-4622Dapeng Oliver Wu3https://orcid.org/0000-0003-1755-0183School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, Hubei, ChinaSchool of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, Hubei, ChinaHubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Computer Science, City University of Hong Kong, Hong Kong, ChinaThis research proposes a stacking machine learning method to accurately predict the compressive strength of recycled concrete. The model integrates eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), and Linear Regression (LR) models, aiming to maximize the prediction accuracy of concrete compressive strength. The model was evaluated using a combination of 63 self-made recycled concrete datasets and 1030 concrete datasets from the UCI Machine Learning Repository. Through optimization based on SHAP values, the new model achieved statistical metrics of RMSE = 1.969, MAE = 1.113, and R2 = 0.987. The comparison and analysis with the existing work show that this method has excellent performance. Additionally, the feature importance analysis based on SHAP values identified the key input variables affecting concrete compressive strength and improved the model’s prediction performance.https://ieeexplore.ieee.org/document/10806804/Concreteensemble learningmachine learningrecycled concrete aggregatestrength prediction |
| spellingShingle | Binghao Pan Wensheng Liu Pan Zhou Dapeng Oliver Wu Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model IEEE Access Concrete ensemble learning machine learning recycled concrete aggregate strength prediction |
| title | Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model |
| title_full | Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model |
| title_fullStr | Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model |
| title_full_unstemmed | Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model |
| title_short | Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model |
| title_sort | predicting the compressive strength of recycled concrete using ensemble learning model |
| topic | Concrete ensemble learning machine learning recycled concrete aggregate strength prediction |
| url | https://ieeexplore.ieee.org/document/10806804/ |
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