Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A...

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.