Method for Extracting Impact Signals in Falling Weight Deflectometer Calibration Based on Frequency Filtering and...

Area of Science:

• Civil Engineering
• Geotechnical Engineering
• Signal Processing

Background:

• Falling Weight Deflectometer (FWD) is crucial for non-destructive highway testing.
• Accurate impact signal identification is vital for FWD calibration and pavement analysis.
• Existing methods struggle with noise interference, affecting FWD result accuracy.

Purpose of the Study:

• To develop a novel method for precise impact point identification in FWD testing.
• To overcome limitations of current methods in noisy conditions.
• To enhance the reliability of pavement bearing capacity evaluations.

Main Methods:

• Frequency domain analysis using FFT and STFT to identify impact signal frequencies.
• Signal reconstruction via IFFT to suppress noise.
• Gradient detection for accurate impact initiation moment determination.

Main Results:

• The proposed method accurately identifies impact points even under significant noise interference.
• Simulated and comparative experiments validate the method's effectiveness.
• Improved signal identification leads to more reliable FWD data.

Conclusions:

• The novel frequency filtering and gradient detection method enhances impact point identification accuracy in FWD testing.
• This technique offers strong environmental adaptability for various engineering impact tests.
• The method provides reliable data support for FWD measurements and pavement analysis.