TY - CONF A1 - Thöns, Sebastian A1 - Döhler, M. ED - del Grosso, A. E. ED - Basso, P. T1 - Structural reliability updating with stochastic subspace damage detection information N2 - Damage detection algorithms as a part of Structural Health Monitoring (SHM) are widely applied in research and industry and have shown their capabilities to efficiently detect structural damages. These algorithms usually compare a model front a safe reference state of a structure to Vibration data from a possibly damaged state. For such a comparison, special properties of real Vibration data introduce uncertainties, such as low signal-to-noise ratios, non-stationary or nonwhite ambient excitation, non-linear behavior and many more. Recently, Statistical damage detection algorithms based on stochastic subspace Identification have been proposed that take into account the uncertainties in the data. Building upon the uncertainty modeling, the next step in the view of the authors is to utilize damage detection algorithm information in the context of the structural reliability theory. Therefore, this paper introduces an approach for the updating of the structural reliability with damage detection algorithm information. Two steps are described namely the determination of a probability of detection (PoD) distribution function for damage detection algorithms accounting for the relevant uncertainties and the concept of Bayesian updating of the structural reliability. The introduced approaches are applied in generic examples. In this way the potential of the utilization of damage detection system information for more reliable structural systems are demonstrated. T2 - EACS 2012 - Smart Structures - 5th European Conference on Structural Control CY - Genoa, Italy DA - 18.06.2012 KW - Stochastic subspace KW - Structural reliability KW - Bayesian updating KW - Probability of detection PY - 2012 SN - 978-88-95023-13-7 IS - Paper #008 SP - 1 EP - 12 PB - Erredi Grafiche Editoriali CY - Genoa AN - OPUS4-26181 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -