An application of statistical process control for structural health monitoring of a highway bridge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a framework to monitor multiple distress condition measurements of structural facilities simultaneously. Multivariate statistical process control methods were employed to: 1) reduce the dimension of data; 2) capture the interrelations among distresses and; 3) rigorously control the false alarm rate.We implemented the methods to monitor the long-term fatigue process of the Hurley Bridge on the border ofWisconsin and Michigan. Real-time measurements were collected on the strains and displacements at critical locations of the bridge while weather and traffic conditions were sampled over the same period to account for exogenous factors. Empirical studies signaled suspicious events with predetermined confidence and identified plausible sources. The results demonstrate that the proposed framework is effective in field practices.

Original languageEnglish (US)
Title of host publicationSafety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
Pages2397-2403
Number of pages7
StatePublished - Dec 1 2013
Event11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 - New York, NY, United States
Duration: Jun 16 2013Jun 20 2013

Publication series

NameSafety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013

Other

Other11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
CountryUnited States
CityNew York, NY
Period6/16/136/20/13

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'An application of statistical process control for structural health monitoring of a highway bridge'. Together they form a unique fingerprint.

Cite this