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When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
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Energy Evaluation and Passive Damage Detection for Structural Health Monitoring in Aerospace Structures Using Machine

Francesco Nicassio1, Flavio Dipietrangelo1, Antonella Gaspari2

  • 1Department of Engineering for Innovation, University of Salento, Via per Monteroni, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms accurately assess impacts on aerospace structures. This artificial intelligence approach predicts impact energy within 10% error and identifies damaged zones with over 95% accuracy for structural health monitoring.

Keywords:
Structural Health Monitoringartificial neural networkimpact characterizationmachine learningregression and classification approaches

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Area of Science:

  • Aerospace Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Structural Health Monitoring (SHM) is increasingly reliant on Artificial Intelligence (AI) in aerospace.
  • Assessing the impact of events on aerospace structures is crucial for safety and maintenance.

Purpose of the Study:

  • To train machine learning algorithms for identifying and characterizing structural impacts on an aerospace aluminum panel.
  • To evaluate the effectiveness of AI in predicting impact severity and detecting pre-existing damage.

Main Methods:

  • Development of impact datasets using vibrational data from piezo sensors on a reinforced plate.
  • Application of shallow neural networks for regression (impact energy) and classification (damage detection).
  • Feature selection guided by physical and mechanical interpretation of phenomena.

Main Results:

  • Accurate prediction of impact energy with less than 10% error.
  • Precise identification of previously damaged zones with over 95% accuracy.
  • Demonstration of computational efficiency for the AI approach.

Conclusions:

  • Machine learning provides a valid and efficient tool for SHM in realistic aerospace structures.
  • AI can effectively characterize impact effects, enhancing structural integrity assessment.
  • The study validates AI for real-time monitoring and damage detection in aerospace applications.