Abnormal data identification and reconstruction model of dam deformation monitoring

(1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;3.National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China;4.Nanjing Water Conservancy Construction Engineering Testing Center Co., Ltd., Nanjing 210036, China)

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    Aiming at the problem of gross errors and abnormal measurements in the deformation monitoring data of concrete dams, a data anomaly identification and reconstruction model is proposed. The association rules are used to quantify the correlation between deformation sequences and water level sequences, and the monitoring data are input into the DBSCAN clustering algorithm to find the abnormal points.The association results are used to classify the data abnormal points into two categories, coarse error points and points reflecting the dam morphology.The points reflecting the dam morphology are retained and the coarse error points are eliminated, and modified wavelet neural network is used to reconstruct the coarse difference data to ensure the integrity of the monitoring sequence. The application results of an arch dam deformation monitoring data show that the model can accurately identify the abnormal values in the monitoring data and can obtain more accurate reconstructed data, providing a new analysis method for the evaluation of measured properties of a dam.

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黎祎,赵二峰,何菁.大坝变形监测异常数据识别和重构模型[J].水利水电科技进展,2023,43(2):109-114.(LI Yi, ZHAO Erfeng, HE Jing. Abnormal data identification and reconstruction model of dam deformation monitoring[J]. Advances in Science and Technology of Water Resources,2023,43(2):109-114.(in Chinese))

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  • Received:April 03,2022
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  • Online: March 10,2023
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