Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning
This study innovatively presents a machine learning framework utilizing advanced artificial intelligence technologies to predict the autogenous shrinkage behavior of ultra-high-performance concrete (UHPC). It provides an integrated approach encompassing data collection, preprocessing, model training...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525000543 |
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| author | Ji Hao Wenbin Jiao Xinpo Xie Dula Man Shengwei Huang |
| author_facet | Ji Hao Wenbin Jiao Xinpo Xie Dula Man Shengwei Huang |
| author_sort | Ji Hao |
| collection | DOAJ |
| description | This study innovatively presents a machine learning framework utilizing advanced artificial intelligence technologies to predict the autogenous shrinkage behavior of ultra-high-performance concrete (UHPC). It provides an integrated approach encompassing data collection, preprocessing, model training, prediction, and result comparison to effectively illustrate the framework. Initially, missing data were interpolated using the modified Akima interpolation method, with visualized results provided for discussion. Subsequently, outlier detection and handling were performed using the Generalized Extreme Studentized Deviate (GESD) technique. At the same time, the analysis of feature contributions across different model predictions clarified the impact of dataset quality on model outcomes and demonstrated the necessity of outlier detection and handling. This study employed grid search to optimize the training of five models: RF, XGBoost, CatBoost, NGBoost, and LightGBM. The comparative analysis of the results revealed that XGBoost achieved the highest prediction accuracy, with R2 values of 0.986 for the training set and 0.937 for the test set. Building on this, the four sets of key ratios of UHPC are quoted as input features to train to derive XGBoost-1, achieving R2 values of 0.917 for the training set and 0.911 for the test set. This approach mitigated variances in raw material compositions, simplified data processing, and enhanced model prediction efficiency. Although its predictive accuracy is slightly lower than that of the initial model, it demonstrates better fitting and generalization performance. Finally, the analysis of correlations among parameters to assess feature importance is crucial for understanding how each parameter influences the auto-shrinkage of UHPC. In conclusion, this framework significantly enhances the accuracy and generalization capabilities of predictive models for UHPC auto-shrinkage and is applicable to other types of concrete materials. |
| format | Article |
| id | doaj-art-4f3a3b3f20a746c18951ca3056f37d48 |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-4f3a3b3f20a746c18951ca3056f37d482025-01-19T06:25:03ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04255Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learningJi Hao0Wenbin Jiao1Xinpo Xie2Dula Man3Shengwei Huang4School of Transportation, Inner Mongolia University, Hohhot 010021, ChinaSchool of Transportation, Inner Mongolia University, Hohhot 010021, ChinaThe First Company of China Eighth Engineering Division Ltd, Jinan 250000, ChinaSchool of Transportation, Inner Mongolia University, Hohhot 010030, ChinaThe First Company of China Eighth Engineering Division Ltd, Jinan 250000, China; Corresponding author.This study innovatively presents a machine learning framework utilizing advanced artificial intelligence technologies to predict the autogenous shrinkage behavior of ultra-high-performance concrete (UHPC). It provides an integrated approach encompassing data collection, preprocessing, model training, prediction, and result comparison to effectively illustrate the framework. Initially, missing data were interpolated using the modified Akima interpolation method, with visualized results provided for discussion. Subsequently, outlier detection and handling were performed using the Generalized Extreme Studentized Deviate (GESD) technique. At the same time, the analysis of feature contributions across different model predictions clarified the impact of dataset quality on model outcomes and demonstrated the necessity of outlier detection and handling. This study employed grid search to optimize the training of five models: RF, XGBoost, CatBoost, NGBoost, and LightGBM. The comparative analysis of the results revealed that XGBoost achieved the highest prediction accuracy, with R2 values of 0.986 for the training set and 0.937 for the test set. Building on this, the four sets of key ratios of UHPC are quoted as input features to train to derive XGBoost-1, achieving R2 values of 0.917 for the training set and 0.911 for the test set. This approach mitigated variances in raw material compositions, simplified data processing, and enhanced model prediction efficiency. Although its predictive accuracy is slightly lower than that of the initial model, it demonstrates better fitting and generalization performance. Finally, the analysis of correlations among parameters to assess feature importance is crucial for understanding how each parameter influences the auto-shrinkage of UHPC. In conclusion, this framework significantly enhances the accuracy and generalization capabilities of predictive models for UHPC auto-shrinkage and is applicable to other types of concrete materials.http://www.sciencedirect.com/science/article/pii/S2214509525000543UHPCMachine learningThe generalized extreme studentized deviate (GESD)The grid search2D PDP |
| spellingShingle | Ji Hao Wenbin Jiao Xinpo Xie Dula Man Shengwei Huang Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning Case Studies in Construction Materials UHPC Machine learning The generalized extreme studentized deviate (GESD) The grid search 2D PDP |
| title | Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning |
| title_full | Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning |
| title_fullStr | Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning |
| title_full_unstemmed | Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning |
| title_short | Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning |
| title_sort | intelligent prediction and understanding of self shrinkage in ultra high performance concrete based on machine learning |
| topic | UHPC Machine learning The generalized extreme studentized deviate (GESD) The grid search 2D PDP |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525000543 |
| work_keys_str_mv | AT jihao intelligentpredictionandunderstandingofselfshrinkageinultrahighperformanceconcretebasedonmachinelearning AT wenbinjiao intelligentpredictionandunderstandingofselfshrinkageinultrahighperformanceconcretebasedonmachinelearning AT xinpoxie intelligentpredictionandunderstandingofselfshrinkageinultrahighperformanceconcretebasedonmachinelearning AT dulaman intelligentpredictionandunderstandingofselfshrinkageinultrahighperformanceconcretebasedonmachinelearning AT shengweihuang intelligentpredictionandunderstandingofselfshrinkageinultrahighperformanceconcretebasedonmachinelearning |