Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods
Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-5309/14/11/3627 |
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| author | Wenchao Li Houmin Li Cai Liu Kai Min |
| author_facet | Wenchao Li Houmin Li Cai Liu Kai Min |
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| description | Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep. |
| format | Article |
| id | doaj-art-e63e77051bd749e88d180fd5d9d657bf |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-e63e77051bd749e88d180fd5d9d657bf2025-08-20T01:53:44ZengMDPI AGBuildings2075-53092024-11-011411362710.3390/buildings14113627Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis MethodsWenchao Li0Houmin Li1Cai Liu2Kai Min3School of Civil Engineering, Architecture and the Environment, Hubei University of Technology, Wuhan 430068, ChinaSchool of Civil Engineering, Architecture and the Environment, Hubei University of Technology, Wuhan 430068, ChinaSchool of Civil Engineering, Architecture and the Environment, Hubei University of Technology, Wuhan 430068, ChinaSchool of Civil Engineering, Architecture and the Environment, Hubei University of Technology, Wuhan 430068, ChinaUnderstanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep.https://www.mdpi.com/2075-5309/14/11/3627concretecreeppredictionmachine learningHSOA optimizationinterpretation |
| spellingShingle | Wenchao Li Houmin Li Cai Liu Kai Min Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods Buildings concrete creep prediction machine learning HSOA optimization interpretation |
| title | Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods |
| title_full | Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods |
| title_fullStr | Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods |
| title_full_unstemmed | Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods |
| title_short | Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods |
| title_sort | concrete creep prediction based on improved machine learning and game theory modeling and analysis methods |
| topic | concrete creep prediction machine learning HSOA optimization interpretation |
| url | https://www.mdpi.com/2075-5309/14/11/3627 |
| work_keys_str_mv | AT wenchaoli concretecreeppredictionbasedonimprovedmachinelearningandgametheorymodelingandanalysismethods AT houminli concretecreeppredictionbasedonimprovedmachinelearningandgametheorymodelingandanalysismethods AT cailiu concretecreeppredictionbasedonimprovedmachinelearningandgametheorymodelingandanalysismethods AT kaimin concretecreeppredictionbasedonimprovedmachinelearningandgametheorymodelingandanalysismethods |