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Development of method for in-service crack detection based on distributed fiber optic sensors

Glisic, Branko, Inaudi, Daniele·2012·Structural Health Monitoring

Abstract

Many bridges worldwide are approaching the end of their lifespan and it is necessary to assess their health condition in order to mitigate risks, prevent disasters, and plan maintenance activities in an optimized manner. Fracture critical bridges are of particular interest since they have only little or no load path redundancy. Structural health monitoring (SHM) has recently emerged as a branch of engineering, which aim is to improve the assessment of structural condition. Distributed optical fiber sensing technology has opened new possibilities in SHM. A distributed deformation sensor (sensing cable) is sensitive at each point of its length to strain changes and cracks. Such a sensor practically monitors a one-dimensional strain field and can be installed over all the length of the monitored structural members, thereby providing with integrity monitoring, i.e. direct detection and characterization (including recognition, localization, and quantification or rating) of local strain changes generated by damage. Integrity monitoring principles are developed and presented in this article. A large scale laboratory test and a real on-site application are briefly presented.

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Metadata

Authors
Glisic, B., & Inaudi, D.
Venue
Structural Health Monitoring
Published
2011-08-09
Volume
11
Issue
2
Pages
161-171
ISSN
  • 1475-9217 (ISSN-L)
  • 1741-3168
OpenAlex
W2108917711
Cited by
161
References
0
FWCI
2.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Development of method for in-service crack detection based on distributed fiber optic sensors. https://app.cochat.ai/papers/10.1177/1475921711414233
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Glisic, B., & Inaudi, D. (2012). Development of method for in-service crack detection based on distributed fiber optic sensors. Structural Health Monitoring. https://doi.org/10.1177/1475921711414233

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.