scholarly journals STRUCTURAL DAMAGE DETECTION OF A MULTI-SPAN CONTINUOUS STEEL-TRUSS BRIDGE FOCUSING ON TRAFFIC VIBRATION

2013 ◽  
Vol 69 (3) ◽  
pp. 557-571 ◽  
Author(s):  
Chul-Woo KIM ◽  
Sotaro KITAUCHI ◽  
Kunitomo SUGIURA ◽  
Mitsuo KAWATANI ◽  
Masayoshi KAI
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Continuous Steel ◽  
Structural Damage Detection ◽  
Steel Truss Bridge ◽  
Steel Truss ◽  
Truss Bridge

scholarly journals Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1271 ◽  
Author(s):  
Asma Alsadat Mousavi ◽  
Chunwei Zhang ◽  
Sami F. Masri ◽  
Gholamreza Gholipour
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Steel Truss Bridge ◽  
Damage Indices ◽  
Bridge Model ◽  
Steel Truss ◽  
Truss Bridge

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

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.

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