Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge

2016 ◽  
Vol 122 ◽  
pp. 156-173 ◽  
Author(s):  
Kai-Chun Chang ◽  
Chul-Woo Kim
Keyword(s):  
Parameter Identification ◽  
Damage Detection ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Truss Bridge

Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach

2021 ◽  
pp. 147592172110459
Author(s):  
Asma A Mousavi ◽  
Chunwei Zhang ◽  
Sami F Masri ◽  
Gholamreza Gholipour
Keyword(s):  
Damage Detection ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Steel Truss Bridge ◽  
Mode Decomposition ◽  
Steel Truss ◽  
Damage States ◽  
Adaptive Noise ◽  
Truss Bridge

This study aims to investigate the performance of a new damage detection method proposed based on the combination of two signal processing techniques which are complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification (CEEMDAN-MUSIC). The proposed damage detection approach begins with determining the power density spectrum, namely, the pseudospectrum, from the acceleration response of a structure. Then, the CEEMDAN algorithm is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs). Furthermore, the MUSIC algorithm is applied to the first IMF of the processed signal to determine the frequency pseudospectrum, prior to and post the damage states of the structure. The effectiveness of the proposed methodology is experimentally validated using a laboratory-scale model of a steel truss bridge exposed to a white noise excitation. The damage states of the truss bridge are implemented by replacing a specified diagonal element with reduced cross-sectional stiffness. The experimental results demonstrate the superiority of the CEEMDAN-MUSIC method in comparison with the performance of pure MUSIC and traditional frequency domain techniques. The advantages of the proposed technique are also discussed in terms of identifying the presence of the damage, addressing its location, and quantifying the damage levels which are summarized as the damage detection and characterization.

Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study

2021 ◽  
pp. 147592172110135
Author(s):  
Asma Alsadat Mousavi ◽  
Chunwei Zhang ◽  
Sami F Masri ◽  
Gholamreza Gholipour
Keyword(s):  
Damage Detection ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Instantaneous Amplitude ◽  
Steel Truss Bridge ◽  
Damage Indices ◽  
Mode Decomposition ◽  
Steel Truss ◽  
Adaptive Noise ◽  
Truss Bridge

Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert–Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been gradually used in structural health monitoring and damage detection. In this article, the application of complete ensemble empirical mode decomposition with adaptive noise technique is investigated to identify the presence, location, and severity of damage on a steel truss bridge model. The target is built at laboratory conditions and experimentally subjected to white noise excitations. By employing complete ensemble empirical mode decomposition with adaptive noise technique, four key features extracted from the intrinsic mode functions, including energy, instantaneous amplitude, unwrapped phase, and instantaneous frequency, are assessed to localization, quantification, and detection of damage both quantitatively and qualitatively. In addition, to further explore the sensitivity of the damage detection approach based on the complete ensemble empirical mode decomposition with adaptive noise technique method, several improved damage indices are proposed based on the combinations of two statistical time-history features, including kurtosis and entropy features with the energy and instantaneous amplitude features of the analyzed signal. The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical mode decomposition and ensemble empirical mode decomposition techniques.

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