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

P. Gardner, R. Fuentes, N. Dervilis, C. Mineo, S. G. Pierce, E.J. Cross, K. Worden

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
104 Downloads (Pure)


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.
Original languageEnglish
Number of pages18
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2182
Early online date14 Sept 2020
Publication statusPublished - 16 Oct 2020


  • ultrasound
  • structural health monitoring
  • non-destructive evaluation
  • machine learning
  • transfer learning
  • compressive sensing


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