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Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
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Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A

Asma Alsadat Mousavi1, Chunwei Zhang1, Sami F Masri2

  • 1School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China.

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Summary

This study evaluates a novel damage detection method for bridges using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Hilbert-Huang Transform (HHT). The energy-based damage index proved most effective for identifying structural damage.

Keywords:
Hilbert–Huang Transform (HHT)artificial neural networkcomplete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)damage detectionsignal processingsteel-truss bridge

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Area of Science:

  • Structural Engineering
  • Vibrational Analysis
  • Nonlinear Dynamics

Background:

  • Complex structures like bridges exhibit nonlinear and non-stationary vibrational behaviors.
  • Hilbert-Huang Transform (HHT) is a key technique for analyzing such responses.
  • Accurate damage detection is crucial for structural integrity and safety.

Purpose of the Study:

  • To evaluate the performance of HHT combined with CEEMDAN for structural damage detection.
  • To propose an Artificial Neural Network (ANN) methodology for damage assessment.
  • To investigate the method's effectiveness on a scaled steel-truss bridge model.

Main Methods:

  • Utilized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition.
  • Applied Hilbert-Huang Transform (HHT) to extract Intrinsic Mode Function (IMF) features.
  • Extracted key features: energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF).
  • Developed an Artificial Neural Network (ANN) model for damage detection, severity classification, and localization.

Main Results:

  • The CEEMDAN-HT-ANN model successfully detected, located, and classified damage in the steel-truss bridge.
  • Damage indices were defined based on extracted IMF features.
  • The energy-based damage index showed superior performance in damage detection compared to IA and unwrapped phase indices.

Conclusions:

  • The CEEMDAN-HT-ANN model offers an efficient approach for structural damage assessment in bridges.
  • Energy-based damage indices are highly effective for identifying structural damage.
  • The methodology holds promise for real-world structural health monitoring applications.