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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

A model-based approach to dispersion and parameter estimation for ultrasonic guided waves.

James S Hall¹, Jennifer E Michaels

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0250, USA.

The Journal of the Acoustical Society of America

|February 9, 2010

Summary

A novel algorithm adaptively estimates ultrasonic guided wave system parameters with minimal assumptions. This method accurately determines wave characteristics, crucial for structural health monitoring applications.

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Area of Science:

Materials Science
Acoustics
Signal Processing

Background:

Ultrasonic guided waves are essential for non-destructive testing.
Accurate system parameter estimation is critical for reliable analysis.
Existing methods often require extensive prior knowledge or assumptions.

Purpose of the Study:

To develop a model-based algorithm for adaptive in situ estimation of ultrasonic guided wave system parameters.
To minimize reliance on a priori information and assumptions.
To create a scalable algorithm for complex multi-mode and multi-receiver scenarios.

Main Methods:

A five-part adaptive algorithm estimates dispersion curves, propagation loss, transducer distances, transmitted signal, and mode weighting coefficients.
The algorithm processes signals from theoretical and finite element models.
Performance is evaluated across simulated test cases with varying noise levels.

Main Results:

The algorithm demonstrated excellent agreement between estimated and actual system parameters.
Modeled and received ultrasonic guided wave signals showed high correlation.
The method proved robust under different noise conditions.

Conclusions:

The presented algorithm effectively estimates ultrasonic guided wave parameters in situ.
It offers a scalable and adaptable solution for structural health monitoring.
The findings support the use of this algorithm for enhanced NDT analysis.