Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM
This research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution characteristics of deformation zones in concrete gravit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10485413/ |
| _version_ | 1797206080563970048 |
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| author | Madiniyeti Jiedeerbieke Tongchun Li Yang Chao Huijun Qi Chaoning Lin |
| author_facet | Madiniyeti Jiedeerbieke Tongchun Li Yang Chao Huijun Qi Chaoning Lin |
| author_sort | Madiniyeti Jiedeerbieke |
| collection | DOAJ |
| description | This research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution characteristics of deformation zones in concrete gravity dams. The model takes into account the intrinsic correlations among monitoring points and achieves more comprehensive deformation monitoring by integrating multiple feature information. Firstly, the improved K-Shape algorithm, which takes into account the time series features and spatial coordinate relationships, is used to cluster and partition the spatial measurement points to better capture the spatial distribution characteristics of the deformation region. Following that, the model hyperparameters undergo iterative optimization using the ZOA optimization algorithm to enhance overall model performance. Finally, a ZOA-BiLSTM modelling process incorporating the correlation characteristics of multiple measurement points is proposed. After validation by engineering examples, the clustering results coincide with the spatial distribution characteristics of dam deformation. Meanwhile, the prediction model has high accuracy and robustness, and predicts the dam deformation from the multi-measurement point correlation dimension, which provides a new and effective method to monitor the overall safety state of the dam. |
| first_indexed | 2024-04-24T09:01:20Z |
| format | Article |
| id | doaj.art-f87fa50e25b24d918f61a0cca28d07e8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T09:01:20Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f87fa50e25b24d918f61a0cca28d07e82024-04-15T23:01:02ZengIEEEIEEE Access2169-35362024-01-0112507105072210.1109/ACCESS.2024.338301610485413Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTMMadiniyeti Jiedeerbieke0https://orcid.org/0009-0009-4472-379XTongchun Li1https://orcid.org/0000-0001-6710-5745Yang Chao2Huijun Qi3Chaoning Lin4College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, ChinaCollege of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Ürümqi, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, ChinaThis research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution characteristics of deformation zones in concrete gravity dams. The model takes into account the intrinsic correlations among monitoring points and achieves more comprehensive deformation monitoring by integrating multiple feature information. Firstly, the improved K-Shape algorithm, which takes into account the time series features and spatial coordinate relationships, is used to cluster and partition the spatial measurement points to better capture the spatial distribution characteristics of the deformation region. Following that, the model hyperparameters undergo iterative optimization using the ZOA optimization algorithm to enhance overall model performance. Finally, a ZOA-BiLSTM modelling process incorporating the correlation characteristics of multiple measurement points is proposed. After validation by engineering examples, the clustering results coincide with the spatial distribution characteristics of dam deformation. Meanwhile, the prediction model has high accuracy and robustness, and predicts the dam deformation from the multi-measurement point correlation dimension, which provides a new and effective method to monitor the overall safety state of the dam.https://ieeexplore.ieee.org/document/10485413/Deformation predictionspatial clusteringbidirectional long-short term memory (BiLSTM)zebra optimization algorithm (ZOA) |
| spellingShingle | Madiniyeti Jiedeerbieke Tongchun Li Yang Chao Huijun Qi Chaoning Lin Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM IEEE Access Deformation prediction spatial clustering bidirectional long-short term memory (BiLSTM) zebra optimization algorithm (ZOA) |
| title | Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM |
| title_full | Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM |
| title_fullStr | Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM |
| title_full_unstemmed | Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM |
| title_short | Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM |
| title_sort | gravity dam deformation prediction model based on i kshape and zoa bilstm |
| topic | Deformation prediction spatial clustering bidirectional long-short term memory (BiLSTM) zebra optimization algorithm (ZOA) |
| url | https://ieeexplore.ieee.org/document/10485413/ |
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