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On damage detection system information for structural systems

Abstract : Damage detection systems (DDS) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. In this paper, an approach is developed and demonstrated to utilize DDS information to update the structural system reliability and to integrate this information in structural system risk and utility analyses. For this aim, a novel performance modelling of DDS building upon their system characteristics and non-destructive testing reliability is introduced. The DDS performance modelling accounts for a measurement system in combination with a damage detection algorithm attached to a structural system in the reference and damage states and is modelled with the probability of indication accounting for type I and II errors. In this way, the basis for DDS performance comparison and assessment is provided accounting for the dependencies between the damage states in a structure. For updating of the structural system reliability, an approach is developed based on Bayesian updating facilitating the use of DDS information on structural system level and thus for a structural system risk analysis. The structural system risk analysis encompasses the static, dynamic, deterioration, reliability and consequence models, which provide the basis for the system model for calculating the direct risks due to component failure and the indirect risks due to system failure. Two case studies with the developed approach demonstrate a high Value of DDS Information due to risk and expected cost reduction.
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Contributor : Michael Döhler Connect in order to contact the contributor
Submitted on : Thursday, July 19, 2018 - 11:36:30 AM
Last modification on : Wednesday, August 10, 2022 - 3:43:06 AM
Long-term archiving on: : Saturday, October 20, 2018 - 1:26:59 PM


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Sebastian Thöns, Michael Döhler, Lijia Long. On damage detection system information for structural systems. Structural Engineering International, 2018, 28 (3), pp.255-268. ⟨10.1080/10168664.2018.1459222⟩. ⟨hal-01844034⟩



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