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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Machine learning inverse surrogates for damage identification in plates based on Lamb waves.

Gabriel L S Silva¹, Bernardo F Junqueira², Daniel A Castello¹

¹Mechanical Engineering Department, Universidade Federal do Rio de Janeiro, Federal University of Rio de Janeiro, Brazil.

|October 5, 2025

Summary

This study introduces a data-driven strategy using Convolution Neural Networks for identifying structural damage in plates. The method accurately locates damage, showing robustness even with varying material properties.

Keywords:

Damage identification Heterogeneous media Lamb wave Machine learning Supervised learning regression Surrogate model

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Area of Science:

Structural Health Monitoring
Machine Learning Applications
Non-Destructive Testing

Background:

Accurate identification of structural damage is crucial for safety and maintenance.
Existing methods for damage identification often rely on physics-based models or complex feature extraction.
Data-driven approaches offer a promising alternative for efficient and accurate damage assessment.

Purpose of the Study:

To develop a black-box, purely data-driven strategy for identifying localized damage in plate-like structures.
To investigate the physical interpretability and accuracy of damage parameter estimation using Convolutional Neural Networks (CNNs).
To evaluate the performance of CNN-based inverse surrogate models under different material property conditions.

Main Methods:

A supervised learning regression task utilizing Convolution Neural Networks (CNNs).
Employing Lamb waves, three actuators, and 16 sensors for data acquisition in an elastic plate model.
Training two inverse surrogate models: one with homogeneous and another with non-homogeneous material properties.
Evaluating damage recovery using overlap metrics to assess accuracy and limitations.

Main Results:

CNNs effectively estimated damage positional parameters with high accuracy.
Damage size and intensity estimation proved more challenging than localization.
The inverse surrogate model trained with non-homogeneous material properties demonstrated robustness against system variability.
Overlap metrics provided insights into the accuracy and limitations of the damage recovery.

Conclusions:

The proposed data-driven CNN strategy is effective for structural damage identification, particularly for localization.
Positional parameter estimation accuracy aligns with literature classifying damage localization as a classification problem.
Robustness against system variability is achievable with surrogate models trained on non-homogeneous material properties.