Machine learning at the interface of structural health monitoring and non-destructive evaluation

Gardner, P. and Fuentes, R. and Dervilis, N. and Mineo, C. and Pierce, S. G. and Cross, E.J. and Worden, K. (2020) Machine learning at the interface of structural health monitoring and non-destructive evaluation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378 (2182). ISSN 1471-2962 (

[thumbnail of Gardner-etal-PTRSA-2020-Machine-learning-at-the-interface-of-structural-health-monitoring]
Text. Filename: Gardner_etal_PTRSA_2020_Machine_learning_at_the_interface_of_structural_health_monitoring.pdf
Accepted Author Manuscript

Download (1MB)| Preview


While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection.


Gardner, P., Fuentes, R., Dervilis, N., Mineo, C. ORCID logoORCID:, Pierce, S. G. ORCID logoORCID:, Cross, E.J. and Worden, K.;