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Related Experiment Video

Updated: Jul 13, 2025

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

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Quantitative damage evaluation of curved plates based on phased array guided wave and deep learning algorithm.

Qi Yuan¹, Ying Wang², Zhongqing Su³

¹School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Guangdong 518055, China; Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region.

|October 13, 2023

Summary

This summary is machine-generated.

This study introduces GW-SHMnet, a framework using phased array guided waves (PAGW) and deep learning for accurate damage size evaluation in curved plates. It enables autonomous quantitative assessment of structural integrity.

Keywords:

Convolutional neural network Curved plate Deep learning Guided wave Phased array Quantitative evaluation Structural health monitoring

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Area of Science:

Structural Health Monitoring
Non-Destructive Testing
Machine Learning Applications

Background:

Phased array guided waves (PAGW) show promise for detecting and locating minor damage in curved plates.
Quantitative damage evaluation is challenging due to difficulties in extracting damage-size-sensitive features.

Purpose of the Study:

To propose a novel integrated framework, GW-SHMnet, for autonomous quantitative evaluation of minor damage in curved plates.
To leverage PAGW, finite element (FE) modeling, and deep learning for this purpose.

Main Methods:

Constructed an FE model to simulate PAGW propagation in curved plates.
Validated the FE model using PAGW experiments on a curved aluminum plate.
Developed a comprehensive FE simulation database for training and testing deep learning models.
Employed deep learning for automatic feature extraction to determine damage size.

Main Results:

The GW-SHMnet framework demonstrated effectiveness, accuracy, and robustness.
Successfully enabled autonomous quantitative evaluation of minor damage.
Validated the capability of deep learning in extracting damage-sensitive features.

Conclusions:

The proposed GW-SHMnet framework offers an effective solution for quantitative damage assessment in curved plates.
This approach advances the field of structural health monitoring for complex geometries.
The integration of PAGW, FE modeling, and deep learning provides a powerful tool for identifying and quantifying structural damage.