Efficient Structural System Reliability Updating with Subspace-Based Damage Detection Information

Michael Döhler, Sebastian Thöns

    Research output: Contribution to conferencePaperResearchpeer-review

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    Abstract

    Damage detection systems and algorithms (DDS and DDA) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. However, the potential of utilizing DDS information for the structural integrity assessment and prognosis is hardly exploited nor treated in scientific literature up to now. In order to utilize the information provided by DDS for the structural performance, usually high computational efforts for the pre-determination of DDS reliability are required. In this paper, an approach for the DDS performance modelling is introduced building upon the non-destructive testing reliability which applies to structural systems and DDS containing a strategy to overcome the high computational efforts for the pre-determination of the DDS reliability. This approach takes basis in the subspace-based damage detection method and builds upon mathematical properties of the damage detection algorithm. Computational efficiency is gained by calculating the probability of damage indication directly without necessitating a pre-determination for all damage states. The developed approach is applied to a static, dynamic, deterioration and reliability structural system model, demonstrating the potentials for utilizing DDS for risk reduction
    Original languageEnglish
    Publication date2016
    Number of pages10
    Publication statusPublished - 2016
    Event8th European Workshop On Structural Health Monitoring (EWSHM 2016) - Bilbao, Spain
    Duration: 5 Jul 20168 Jul 2016
    Conference number: 8

    Conference

    Conference8th European Workshop On Structural Health Monitoring (EWSHM 2016)
    Number8
    Country/TerritorySpain
    CityBilbao
    Period05/07/201608/07/2016

    Keywords

    • reliability updating
    • performance models
    • damage detection
    • subspace methods

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