On the impact of prior perception on bridge health diagnosis

C. Cappello, F. Bruschetta, D. Zonta, S. Maestranzi, R. Zandonini, M. Pozzi, B. Glisic, D. Inaudi, D. Posenato

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

Abstract

We illustrate an application of Bayesian logic to analysis of monitoring data and to inference of structural condition. The case study is a cable-stayed bridge, having a composite steel-concrete deck, supported by 12 cables. To monitor load redistribution, the owner installed a monitoring system that includes fiberoptic sensors. These sensors measure changes in deformation with respect to the value at installation. After one year of operation, which included maintenance on the interrogation unit, the data showed an apparent contraction of the cables. We discuss to what extent a rational agent is prone to accept the sensor response as the result of the real mechanical behavior of the bridge versus a mere malfunction of the interrogation unit. Using Bayesian logic as a tool to combine prior belief with sensor data, we highlight how the extent of prior knowledge can alter the final engineering perception of the current state of the bridge.

Original languageEnglish (US)
Title of host publicationBridge Maintenance, Safety, Management and Life Extension - Proceedings of the 7th International Conference of Bridge Maintenance, Safety and Management, IABMAS 2014
PublisherTaylor and Francis - Balkema
Pages648-655
Number of pages8
ISBN (Print)9781138001039
DOIs
StatePublished - Jan 1 2014
Event7th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2014 - Shanghai, China
Duration: Jul 7 2014Jul 11 2014

Publication series

NameBridge Maintenance, Safety, Management and Life Extension - Proceedings of the 7th International Conference of Bridge Maintenance, Safety and Management, IABMAS 2014

Other

Other7th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2014
CountryChina
CityShanghai
Period7/7/147/11/14

All Science Journal Classification (ASJC) codes

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

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