Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning
The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (&...
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MDPI AG
2024-11-01
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| author | Pengfei Gao Yuanyuan Song Jian Wang Zhiyong Yang Kai Wang Yongyu Yuan |
| author_facet | Pengfei Gao Yuanyuan Song Jian Wang Zhiyong Yang Kai Wang Yongyu Yuan |
| author_sort | Pengfei Gao |
| collection | DOAJ |
| description | The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (<i>YN</i>), the replacement ratio of recycled coarse aggregates (<i>r</i>), the bending load level (<i>L</i>), the carbonation time (<i>t</i>) and temperature (<i>T</i>) of RAC, and the replacement ratio of carbonated recycled fine aggregates (<i>f</i>), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was <i>r</i> > <i>t</i> > <i>f</i> > <i>T</i> > <i>L</i> > <i>YN</i>. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC. |
| format | Article |
| id | doaj-art-0537d643cedf473088cac092158849db |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-11-01 |
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| series | Buildings |
| spelling | doaj-art-0537d643cedf473088cac092158849db2025-08-20T01:53:44ZengMDPI AGBuildings2075-53092024-11-011411360810.3390/buildings14113608Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine LearningPengfei Gao0Yuanyuan Song1Jian Wang2Zhiyong Yang3Kai Wang4Yongyu Yuan5Inspection and Certification Co., Ltd. MCC, Beijing 100088, ChinaSchool of Civil Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Ocean Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaThe chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (<i>YN</i>), the replacement ratio of recycled coarse aggregates (<i>r</i>), the bending load level (<i>L</i>), the carbonation time (<i>t</i>) and temperature (<i>T</i>) of RAC, and the replacement ratio of carbonated recycled fine aggregates (<i>f</i>), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was <i>r</i> > <i>t</i> > <i>f</i> > <i>T</i> > <i>L</i> > <i>YN</i>. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC.https://www.mdpi.com/2075-5309/14/11/3608recycled aggregate concretechloride ion permeabilityelectric fluxmachine learningprediction model |
| spellingShingle | Pengfei Gao Yuanyuan Song Jian Wang Zhiyong Yang Kai Wang Yongyu Yuan Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning Buildings recycled aggregate concrete chloride ion permeability electric flux machine learning prediction model |
| title | Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning |
| title_full | Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning |
| title_fullStr | Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning |
| title_full_unstemmed | Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning |
| title_short | Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning |
| title_sort | prediction model for the chloride ion permeability resistance of recycled aggregate concrete based on machine learning |
| topic | recycled aggregate concrete chloride ion permeability electric flux machine learning prediction model |
| url | https://www.mdpi.com/2075-5309/14/11/3608 |
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