Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models
Elastic modulus, crucial for assessing material stiffness and structural deformation, has recently gained popularity in predictions using data-driven methods. However, research systematically comparing different machine learning models under the same conditions, especially for ultra-high-performance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/10/3184 |
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| author | Chaohui Zhang Peng Liu Tiantian Song Bin He Wei Li Yuansheng Peng |
| author_facet | Chaohui Zhang Peng Liu Tiantian Song Bin He Wei Li Yuansheng Peng |
| author_sort | Chaohui Zhang |
| collection | DOAJ |
| description | Elastic modulus, crucial for assessing material stiffness and structural deformation, has recently gained popularity in predictions using data-driven methods. However, research systematically comparing different machine learning models under the same conditions, especially for ultra-high-performance concrete (UHPC), remains limited. In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. The results showed that XGBoost demonstrated the highest accuracy in predictions with large training datasets, followed by KNNs. For smaller training datasets, Decision Tree exhibited the greatest accuracy, while XGBoost was the second-best performing model. Linear regression displayed the lowest accuracy. XGBoost demonstrated the most potential for accurately predicting the elastic modulus of UHPC, particularly when a comprehensive dataset is available for model training. The optimized XGBoost exhibited better predictive performance than fitting equations for different UHPC formulations. The findings of this study provide valuable insights for researchers and engineers working on the data-driven design and characterization of UHPC. |
| format | Article |
| id | doaj-art-41874b70d7a5422f9d0f5be6ef9dc930 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-41874b70d7a5422f9d0f5be6ef9dc9302025-08-20T02:11:03ZengMDPI AGBuildings2075-53092024-10-011410318410.3390/buildings14103184Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning ModelsChaohui Zhang0Peng Liu1Tiantian Song2Bin He3Wei Li4Yuansheng Peng5Shenzhen Metro Group Co., Ltd., Shenzhen 518026, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaShenzhen Metro Group Co., Ltd., Shenzhen 518026, ChinaShenzhen Metro Group Co., Ltd., Shenzhen 518026, ChinaShenzhen Metro Group Co., Ltd., Shenzhen 518026, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaElastic modulus, crucial for assessing material stiffness and structural deformation, has recently gained popularity in predictions using data-driven methods. However, research systematically comparing different machine learning models under the same conditions, especially for ultra-high-performance concrete (UHPC), remains limited. In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. The results showed that XGBoost demonstrated the highest accuracy in predictions with large training datasets, followed by KNNs. For smaller training datasets, Decision Tree exhibited the greatest accuracy, while XGBoost was the second-best performing model. Linear regression displayed the lowest accuracy. XGBoost demonstrated the most potential for accurately predicting the elastic modulus of UHPC, particularly when a comprehensive dataset is available for model training. The optimized XGBoost exhibited better predictive performance than fitting equations for different UHPC formulations. The findings of this study provide valuable insights for researchers and engineers working on the data-driven design and characterization of UHPC.https://www.mdpi.com/2075-5309/14/10/3184elastic modulusUHPCmachine learningXGBoostdecision tree |
| spellingShingle | Chaohui Zhang Peng Liu Tiantian Song Bin He Wei Li Yuansheng Peng Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models Buildings elastic modulus UHPC machine learning XGBoost decision tree |
| title | Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models |
| title_full | Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models |
| title_fullStr | Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models |
| title_full_unstemmed | Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models |
| title_short | Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models |
| title_sort | elastic modulus prediction of ultra high performance concrete with different machine learning models |
| topic | elastic modulus UHPC machine learning XGBoost decision tree |
| url | https://www.mdpi.com/2075-5309/14/10/3184 |
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