Probabilistic damage detection based on large area electronics sensing sheets

Yao Yao, Branko Glisic

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


Reliable early-stage damage detection and characterization requires continuous sensing over large areas of structure. The limitations with current sensing technologies lies in the fact that they either have high cost and insufficient spatial resolution, or rely on complex algorithms that are challenged by varying environmental and loading conditions. This paper addresses the need for direct sensing where anomalies are sensed at close proximity through a dense array of sensors, and proposes one approach for sensor network design. This approach is directly applicable to innovative sensing sheet based on large area electronics (LAE), which enables practical implementation of dense arrays of sensors. However, although the sensors are densely spaced in the sensing sheet, there are still some non-instrumented spaces between them and these spaces are not sensitive to damage. In this research, a probabilistic approach based on Monte Carlo (MC) simulations is researched to determine the probability that damage of certain size that occurs within the area covered by the sensing sheet can be detected with a given sensor network. Based on these Probability of Detection (POD) functions, it was possible to assess the reliability of sensing sheets for crack detection and to establish general principles for the design of sensing sheets.

Original languageAmerican English
Number of pages8
StatePublished - 2014
Event7th European Workshop on Structural Health Monitoring, EWSHM 2014 - Nantes, France
Duration: Jul 8 2014Jul 11 2014


Other7th European Workshop on Structural Health Monitoring, EWSHM 2014

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Building and Construction
  • Computer Science Applications
  • Civil and Structural Engineering


  • Direct sensing
  • Large area electronics
  • Monte Carlo method
  • Probabilistic damage detection
  • Structural health monitoring

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