Damage detection in a reinforced concrete slab using outlier analysis

Bilal Ali Qadri, Dmitri Tcherniak, Martin Dalgaard Ulriksen, Lars Damkilde

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)
118 Downloads (Pure)


From a strict academic point of view, the task of globally detecting structural damage, albeit still a topic of active research, has been resolved with reasonable success by adapting approaches from the fields of statistical pattern recognition and machine learning. As a result, substantial attention is now addressed to bridging the gap between research and industrial use, and numerous successful application studies have been reported for different types of engineering structures. The present paper follows along this path and contributes with an experimental study concerning damage detection in a reinforced concrete slab based on conventional outlier analysis of captured vibration response. The concrete slab is investigated in five measurement setups, one reference, with an initial dent, and four damaged states by extending the dent with blank shots fired into the surface. In each state, the structure is subjected to controlled impulse excitation from an installed electro-mechanical actuator. The impulse response is captured by 14 accelerometers distributed evenly over the concrete slab. The entries in the covariance matrix of these accelerations are employed as the feature in the outlier analysis, where the damage index is the Mahalanobis metric.
Original languageEnglish
Title of host publicationProceedings of the 9th European Workshop on Structural Health Monitoring
Number of pages7
PublisherNDT net
Publication date2018
Article number95
Publication statusPublished - 2018
Event9th European Workshop on Structural Health Monitoring - Manchester, United Kingdom
Duration: 10 Jul 201813 Jul 2018


Conference9th European Workshop on Structural Health Monitoring
Country/TerritoryUnited Kingdom
Internet address


  • Structural Health Monitoring
  • Damage detection
  • Damage identification
  • Outlier analysis


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