Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam
In recent years, classical statistical models and machine learning models have been developed in parallel in the field of dam safety monitoring. However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. In this study, multiple linear re...
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Editorial Office of Pearl River
2025-03-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails?columnId=76417573&Fpath=home&index=0 |
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| author | HUANG Song WU Jie FANG Zhanchao CHU Huaping WU Yan'gang XUE Zilong HE Linbo |
| author_facet | HUANG Song WU Jie FANG Zhanchao CHU Huaping WU Yan'gang XUE Zilong HE Linbo |
| author_sort | HUANG Song |
| collection | DOAJ |
| description | In recent years, classical statistical models and machine learning models have been developed in parallel in the field of dam safety monitoring. However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. In this study, multiple linear regression, stepwise regression, and random forest algorithm were used to establish models for the crack opening degree of a concrete gravity dam based on the monitoring data of the crack opening degree of the concrete gravity dam. The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. The random forest model has the best predictive ability (determination coefficient (<italic>R</italic><sup>2</sup>) is 0.995; root mean square error (<italic>E</italic><sub>RMS</sub>) and mean absolute error (<italic>E</italic><sub>MA</sub>) are 0.174 mm and 0.124 mm, respectively), followed by the stepwise regression model (<italic>R</italic><sup>2</sup> is 0.989; <italic>E</italic><sub>RMS</sub><italic> </italic>and <italic>E</italic><sub>MA</sub><italic> </italic>are 0.192 mm and 0.151 mm). Three models both indicate that the temperature component is the main factor affecting the crack opening degree of the concrete gravity dam; by decomposing the multiple linear regression model item by item, the variation patterns of crack opening degree of the concrete gravity dam, temperature component, hydraulic pressure component, and time component are obtained. This study can provide a reference for the operation and management of the concrete gravity dam and the construction of the forecasting, early warning, drilling, and emergency plan (FEDE) platform, with a relatively high theoretical and practical significance. |
| format | Article |
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| institution | OA Journals |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-b4b96e5ef9d9433dbded1d67094bd78a2025-08-20T01:57:39ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352025-03-0146253176417573Comparative Study on Prediction Models for Crack Opening Degree in Concrete DamHUANG SongWU JieFANG ZhanchaoCHU HuapingWU Yan'gangXUE ZilongHE LinboIn recent years, classical statistical models and machine learning models have been developed in parallel in the field of dam safety monitoring. However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. In this study, multiple linear regression, stepwise regression, and random forest algorithm were used to establish models for the crack opening degree of a concrete gravity dam based on the monitoring data of the crack opening degree of the concrete gravity dam. The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. The random forest model has the best predictive ability (determination coefficient (<italic>R</italic><sup>2</sup>) is 0.995; root mean square error (<italic>E</italic><sub>RMS</sub>) and mean absolute error (<italic>E</italic><sub>MA</sub>) are 0.174 mm and 0.124 mm, respectively), followed by the stepwise regression model (<italic>R</italic><sup>2</sup> is 0.989; <italic>E</italic><sub>RMS</sub><italic> </italic>and <italic>E</italic><sub>MA</sub><italic> </italic>are 0.192 mm and 0.151 mm). Three models both indicate that the temperature component is the main factor affecting the crack opening degree of the concrete gravity dam; by decomposing the multiple linear regression model item by item, the variation patterns of crack opening degree of the concrete gravity dam, temperature component, hydraulic pressure component, and time component are obtained. This study can provide a reference for the operation and management of the concrete gravity dam and the construction of the forecasting, early warning, drilling, and emergency plan (FEDE) platform, with a relatively high theoretical and practical significance.http://www.renminzhujiang.cn/thesisDetails?columnId=76417573&Fpath=home&index=0concrete damcrack opening degreestatistical modelrandom forest algorithm |
| spellingShingle | HUANG Song WU Jie FANG Zhanchao CHU Huaping WU Yan'gang XUE Zilong HE Linbo Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam Renmin Zhujiang concrete dam crack opening degree statistical model random forest algorithm |
| title | Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam |
| title_full | Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam |
| title_fullStr | Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam |
| title_full_unstemmed | Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam |
| title_short | Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam |
| title_sort | comparative study on prediction models for crack opening degree in concrete dam |
| topic | concrete dam crack opening degree statistical model random forest algorithm |
| url | http://www.renminzhujiang.cn/thesisDetails?columnId=76417573&Fpath=home&index=0 |
| work_keys_str_mv | AT huangsong comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT wujie comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT fangzhanchao comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT chuhuaping comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT wuyangang comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT xuezilong comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam AT helinbo comparativestudyonpredictionmodelsforcrackopeningdegreeinconcretedam |