Damage Detection and Deteriorating Structural Systems - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2017

Damage Detection and Deteriorating Structural Systems

Résumé

This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented. The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the information and its precision on structural system level, facilitating actions to ensure the structural integrity and facilitating to describe the structural system performance and its function-ality throughout the service life. The structural system performance is described with its functionality, its deterioration and its behavior under extreme loading. The structural system reliability given the damage detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection information is then calculated as the difference between the expected benefits and risks utilizing the damage detection information or not. With an application example of the developed approach based on a deteriorating Pratt truss system, the value of damage detection information is determined , demonstrating the potential of risk reduction and expected cost reduction.
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Dates et versions

hal-01589545 , version 1 (18-09-2017)

Identifiants

Citer

Lijia Long, Sebastian Thöns, Michael Döhler. Damage Detection and Deteriorating Structural Systems. IWSHM - 11th International Workshop on Structural Health Monitoring, Sep 2017, Stanford, United States. ⟨10.12783/shm2017/13997⟩. ⟨hal-01589545⟩
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