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Deformation is one of the critical response quantities reflecting the structural safety of dams. To enhance outlier identification and denoising in dam deformation monitoring data, this study proposes a novel preprocessing method based on optimized Variational Mode Decomposition (VMD) and Kernel Density Estimation (KDE). The approach systematically processes data in three steps: First, VMD decomposes raw data into intrinsic mode functions without recursion. The parallel Jaya algorithm is used to adaptively optimize VMD parameters for improved decomposition. Second, the intrinsic mode functions containing outlier and noise characteristics are identified and separated using sample entropy and correlation coefficients. Finally, KDE thresholds are applied for outlier localization, while a data superposition method ensures effective denoising. Validation using simulated deformation data and Global Navigation Satellite Systems (GNSS)-based observed horizontal deformation from dam engineering demonstrates the method’s robustness in accurately identifying outliers and denoising data, achieving superior preprocessing performance.
Details
Outliers (statistics);
Structural engineering;
Accuracy;
Wavelet transforms;
Structural safety;
Deformation;
Optimization;
Signal processing;
Deformation effects;
Spectrum allocation;
Localization;
Density;
Correlation coefficients;
Correlation coefficient;
Statistical analysis;
Data analysis;
Preprocessing;
Lagrange multiplier;
Fourier transforms;
Noise reduction;
Dam engineering;
Algorithms;
Decomposition;
Methods;
Satellite observation;
Global navigation satellite system
; Lin, Chaoning 2
; Gu, Yanchang 1 ; Sheng, Jinbao 1 ; Mohammad Amin Hariri-Ardebili 3
1 Dam Safety Management Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China;
2 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
3 Department of Civil, Environment, and Architectural Engineering, University of Colorado, Boulder, CO 80309, USA;