A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM
This paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the paramete...
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/3/357 |
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| author | Pei Liu Hao Gu Chongshi Gu Yanbo Wang |
| author_facet | Pei Liu Hao Gu Chongshi Gu Yanbo Wang |
| author_sort | Pei Liu |
| collection | DOAJ |
| description | This paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the parameters K and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> of the VMD algorithm. It combines the variance of the modified mode with the sample entropy of these data as the objective function, effectively converting monitoring data into a stable signal while retaining essential characteristic variation. Data are reformatted into a three-dimensional structure and partitioned into training and testing sets. The AttLSTM network was applied to forecast deformation, and results were validated using practical engineering cases. The performance of the proposed model was compared against that of four other models: LSTM, VMD-LSTM, attention LSTM, and VMD-AttLSTM models. Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy. |
| format | Article |
| id | doaj-art-e8e90e861a7b46a0bcaa241c821c801d |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-e8e90e861a7b46a0bcaa241c821c801d2025-08-20T02:48:09ZengMDPI AGBuildings2075-53092025-01-0115335710.3390/buildings15030357A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTMPei Liu0Hao Gu1Chongshi Gu2Yanbo Wang3The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, ChinaThis paper presents a deformation prediction model for concrete dams that integrates a reptile search algorithm (RSA), a Variational Mode Decomposition (VMD) algorithm, and a long short-term memory network model with attention mechanism (AttLSTM). This model utilizes the RSA to optimize the parameters K and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> of the VMD algorithm. It combines the variance of the modified mode with the sample entropy of these data as the objective function, effectively converting monitoring data into a stable signal while retaining essential characteristic variation. Data are reformatted into a three-dimensional structure and partitioned into training and testing sets. The AttLSTM network was applied to forecast deformation, and results were validated using practical engineering cases. The performance of the proposed model was compared against that of four other models: LSTM, VMD-LSTM, attention LSTM, and VMD-AttLSTM models. Analysis of the five evaluation criteria revealed that the RSA can better optimize the parameters of the VMD algorithm. Consequently, the proposed model demonstrates superior noise reduction capabilities and improved prediction accuracy.https://www.mdpi.com/2075-5309/15/3/357deformation prediction modelreptile search algorithmvariable mode decompositionlong short-term memory network model with attention mechanism |
| spellingShingle | Pei Liu Hao Gu Chongshi Gu Yanbo Wang A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM Buildings deformation prediction model reptile search algorithm variable mode decomposition long short-term memory network model with attention mechanism |
| title | A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM |
| title_full | A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM |
| title_fullStr | A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM |
| title_full_unstemmed | A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM |
| title_short | A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM |
| title_sort | deformation prediction model for concrete dams based on rsa vmd attlstm |
| topic | deformation prediction model reptile search algorithm variable mode decomposition long short-term memory network model with attention mechanism |
| url | https://www.mdpi.com/2075-5309/15/3/357 |
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