Reconstruction algorithm for probabilistic inspection of damage (RAPID) in composites

Morteza Tabatabaeipour, Jan Hettler, Steven Delrue, Koen Van Den Abeele

Research output: Contribution to conferencePaperpeer-review


Carbon-fiber-reinforced composites (CFRP) have become one of the most important structural materials in the last two decades. It is extensively used especially in the automotive and aerospace industry. Together with their popularity, important questions arise about their damage resistance and potential damage monitoring techniques. Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) tomography is a relatively fast method capable to provide information about the presence of damage, as well as about the damage location. The method uses the signal difference coefficient (SDC) value to describe the probability of the damage presence between the various transmitter-receiver pairs in an ultrasonic sparse array. The combination of SDC with a priori spatial probability distribution finally allows one to construct the tomographic image. The method can be applied both for linear defects, using a reference set of signals, or for non-linear signals by evaluating the sparse array signal matrix at different amplitudes. We will illustrate the methodology using experimental and numerical results of RAPID on an impacted and delaminated CFRP plates instrumented with sparse arrays up to 16 elements
Original languageEnglish
Publication statusPublished (in print/issue) - 10 Oct 2014
EventEuropean Conference on Non-Destructive Testing - Prague, Czech Republic
Duration: 6 Oct 20148 Oct 2014


ConferenceEuropean Conference on Non-Destructive Testing
Abbreviated titleECNDT 2014
Country/TerritoryCzech Republic

Bibliographical note

European Conference on Non-Destructive Testing , ECNDT 2014 ; Conference date: 06-10-2014 Through 08-10-2014


  • carbon-fiber-reinforced composites (CFRP)
  • composite
  • ultrasonic
  • NDT
  • ultrasound imaging
  • probabalistic imaging
  • sparse arrays
  • Structural health monitoring


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