Journal of Engineering in Industrial Research

h-index: 18     i10-index: 25

Application of Artificial Intelligence in Aerospace Structural Health Monitoring: A Systematic Review and Future Perspectives

Document Type : Review Article

Authors

Micheal Abimbola Oladosu 1
Moses Adondua Abah 2
Nasiru Musa 3
Olaide Ayokunmi Oladosu 4

1 Department of Computer Science, Faculty of Sciences, University of Lagos, Akoka, Lagos, Nigeria

2 Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University of Wukari, Wukari, Taraba State, Nigeria

3 Department of Mission Planning, Directorate of Earth Observation, Defence Space Administration, Federal Capital Territory, Abuja Nigeria

4 Department of Computer Science, Faculty of Science and Technology, Babcock University, Ilishan-Remo, Nigeria

https://doi.org/10.48309/jeires.2026.546699.1313
Abstract
Aerospace structural health monitoring (SHM) has evolved significantly with the integration of artificial intelligence (AI) technologies, transforming traditional maintenance paradigms from reactive to predictive approaches. The complexity of modern aerospace structures necessitates advanced monitoring systems capable of real-time damage detection, prognosis, and decision-making. This systematic review examines the current state-of-the-art applications of AI in aerospace SHM, evaluates existing methodologies, identifies research gaps, and proposes future directions for enhanced structural integrity assessment. A comprehensive literature search was conducted across multiple databases (Web of Science, Scopus, IEEE Xplore, and PubMed) covering publications from 2018 to 2024. Studies were selected based on predefined inclusion criteria focusing on AI applications in aerospace SHM, including machine learning, deep learning, and hybrid approaches. The review analyzed 127 relevant publications, revealing significant advancements in AI-driven SHM technologies. Machine learning algorithms demonstrated 85-95% accuracy in damage detection, while deep learning approaches achieved up to 98% accuracy in complex pattern recognition tasks. Hybrid AI systems showed superior performance in real-time monitoring applications with reduced false alarm rates. The integration of AI in aerospace SHM has shown tremendous potential for improving safety, reducing maintenance costs, and extending aircraft service life. However, challenges remain in data standardization, model interpretability, and regulatory compliance. Future research should focus on developing explainable AI models, enhancing edge computing capabilities, and establishing industry-wide standards.

Graphical Abstract

Application of Artificial Intelligence in Aerospace Structural Health Monitoring: A Systematic Review and Future Perspectives

Keywords

Artificial intelligence
Aerospace
Structural health monitoring
Machine learning
Deep learning

Subjects

OPEN ACCESS

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Journal of Engineering in Industrial Research
Volume 7, Issue 3
Summer 2026
Pages 100-119

  • Receive Date 12 September 2025
  • Revise Date 02 December 2025
  • Accept Date 25 December 2025

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