| 大坝变形监测异常数据识别和重构模型 |
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| 引用本文： | 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. |
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| 作者姓名： | 黎祎 赵二峰 何菁 |
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| 作者单位： | 河海大学水文水资源与水利工程科学国家重点实验室,江苏 南京210098;河海大学水利水电学院,江苏 南京210098;河海大学水资源高效利用与工程安全国家工程研究中心,江苏 南京210098;南京市水利建筑工程检测中心有限公司,江苏 南京210036 |
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| 基金项目： | 国家自然科学基金(U2243223, 51739003, 52079046)；中央高校基本科研业务费专项(B210202017) |
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| 摘 要： | 针对混凝土坝变形监测数据中的粗差和异常测值问题，提出了一种数据异常识别和重构模型。模型利用关联规则量化变形序列与水位序列的关联性，将监测数据输入DBSCAN聚类算法寻找异常点，利用关联结果将监测数据异常点分为粗差点与反映大坝性态点两类，保留反映大坝性态点，剔除粗差点，并利用改进的小波神经网络对粗差数据进行重构，保证监测序列完整性。某拱坝变形监测数据验证结果表明，该模型可以准确识别监测数据中的异常值，并能够获得更为准确的重构数据，为大坝实测性态评价提供了新的分析方法。
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| 关 键 词： | 异常数据 数据重构 关联规则 DBSCAN算法 小波神经网络 |
| 收稿时间： | 2022/4/3 0:00:00 |
Abnormal data identification and reconstruction model of dam deformation monitoring |
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| 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. |
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| Authors: | LI Yi ZHAO Erfeng HE Jing |
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| Affiliation: | State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China; Nanjing Water Conservancy Construction Engineering Testing Center Co., Ltd., Nanjing 210036, China |
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| Abstract: | 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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