A New Prediction Model of Dam Deformation and Successful Application
In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/5/818 |
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| author | Shuangping Li Bin Zhang Meng Yang Senlin Li Zuqiang Liu |
| author_facet | Shuangping Li Bin Zhang Meng Yang Senlin Li Zuqiang Liu |
| author_sort | Shuangping Li |
| collection | DOAJ |
| description | In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. By studying the measured deformation data of an arch dam and comparing the performance parameters of various models, the superiority and universality of the proposed method are verified. Dam deformation monitoring data are of great significance to describe the operation behavior of dams. It is significant for us to optimize the health monitoring of dam safety structures and ensure dam safety and realize social harmony in our country. |
| format | Article |
| id | doaj-art-778c355ddcde43efabb1b4e380b290bc |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-778c355ddcde43efabb1b4e380b290bc2025-08-20T02:53:02ZengMDPI AGBuildings2075-53092025-03-0115581810.3390/buildings15050818A New Prediction Model of Dam Deformation and Successful ApplicationShuangping Li0Bin Zhang1Meng Yang2Senlin Li3Zuqiang Liu4Changjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 430010, ChinaChangjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 430010, ChinaNanjing Hydraulic Research Institute, Nanjing 210029, ChinaNanjing Hydraulic Research Institute, Nanjing 210029, ChinaChangjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 430010, ChinaIn most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. By studying the measured deformation data of an arch dam and comparing the performance parameters of various models, the superiority and universality of the proposed method are verified. Dam deformation monitoring data are of great significance to describe the operation behavior of dams. It is significant for us to optimize the health monitoring of dam safety structures and ensure dam safety and realize social harmony in our country.https://www.mdpi.com/2075-5309/15/5/818dam deformation predictionensemble empirical mode decompositionXGBoostLASSO feature selectionoptimal influence factor |
| spellingShingle | Shuangping Li Bin Zhang Meng Yang Senlin Li Zuqiang Liu A New Prediction Model of Dam Deformation and Successful Application Buildings dam deformation prediction ensemble empirical mode decomposition XGBoost LASSO feature selection optimal influence factor |
| title | A New Prediction Model of Dam Deformation and Successful Application |
| title_full | A New Prediction Model of Dam Deformation and Successful Application |
| title_fullStr | A New Prediction Model of Dam Deformation and Successful Application |
| title_full_unstemmed | A New Prediction Model of Dam Deformation and Successful Application |
| title_short | A New Prediction Model of Dam Deformation and Successful Application |
| title_sort | new prediction model of dam deformation and successful application |
| topic | dam deformation prediction ensemble empirical mode decomposition XGBoost LASSO feature selection optimal influence factor |
| url | https://www.mdpi.com/2075-5309/15/5/818 |
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