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Temperature Dependent Deformation

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...

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Domain-Adaptive Graph Attention Semi-Supervised Network for Temperature-Resilient SHM of Composite Plates.

Nima Rezazadeh^1,2, Alessandro De Luca², Donato Perfetto²

¹School of Architecture, Built Environment, Computing and Engineering, Birmingham City University, Birmingham B4 7XG, UK.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This study presents GAT-CAMDA, a framework for structural health monitoring (SHM) of composites. It ensures robust damage detection across varying temperatures using Graph Attention Networks (GATs) and domain adaptation.

Keywords:

composite materials domain adaptation (DA)explainability graph attention networks (GATs)structural health monitoring (SHM)temperature variability

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Area of Science:

Materials Science
Engineering
Artificial Intelligence

Background:

Structural health monitoring (SHM) is crucial for composite materials.
Temperature variations pose significant challenges to SHM accuracy.
Existing methods struggle with domain shifts caused by environmental factors.

Purpose of the Study:

To introduce GAT-CAMDA, a novel framework for SHM of composite materials.
To address temperature-induced variability and ensure robust damage detection.
To enhance the interpretability and scalability of SHM systems.

Main Methods:

Utilizing Graph Attention Networks (GATs) for feature extraction.
Employing advanced domain adaptation (DA) techniques, including MMD and CORAL losses with adversarial learning.
Implementing a data augmentation strategy for damage extrapolation.
Optimizing hyperparameters using Optuna for performance enhancement and explainability.

Main Results:

Achieved 95.83% classification accuracy on a benchmark dataset.
Demonstrated scalable alignment of feature distributions across temperature domains.
Showcased the importance of specific sensors through GAT attention mechanisms.
Validated the effectiveness of Optuna for both accuracy refinement and parameter impact analysis.

Conclusions:

GAT-CAMDA offers a significant advancement in SHM for composite materials.
The framework provides precise, interpretable, and scalable damage diagnostics.
The study highlights the potential of integrating GATs and DA for complex operational environments.