An Internal Defect Detection Algorithm for Concrete Blocks Based on Local Mean Decomposition-Singular Value...

Area of Science:

• Civil Engineering
• Materials Science
• Non-Destructive Testing

Background:

• Laser Doppler Vibrometry (LDV) is crucial for concrete internal defect detection.
• Low signal-to-noise ratios (SNR) in LDV signals, caused by concrete heterogeneity and surface roughness, hinder precise defect characterization.
• Existing methods struggle to accurately discern defect signal characteristics.

Purpose of the Study:

• To develop an advanced algorithm for enhanced internal defect detection in concrete using LDV.
• To address the challenges posed by low SNR and complex material properties in concrete inspection.
• To accurately locate and characterize internal defects in concrete structures.

Main Methods:

• A novel algorithm integrating Local Mean Decomposition-Singular Value Decomposition (LMD-SVD) for signal denoising.
• Application of Weighted Spatial-Spectral Entropy (WSSE) to analyze frequency distribution in the scan plane.
• Characterization of defects based on modal vibration patterns of estimated resonant frequencies.

Main Results:

• The LMD-SVD algorithm effectively reduces noise interference in LDV signals.
• WSSE analysis reveals non-uniform energy distribution for defect resonance frequencies, enabling their estimation.
• The proposed algorithm successfully pinpointed the location and shape of simulated shallow defects in concrete blocks.

Conclusions:

• The combined LMD-SVD and WSSE algorithm significantly improves the accuracy of internal defect detection in concrete via LDV.
• This method offers a practical and effective solution for identifying shallow defects in real-world engineering scenarios.
• The study highlights the potential of advanced signal processing techniques for non-destructive evaluation of concrete structures.