TY - JOUR A1 - Küttenbaum, Stefan A1 - Maack, Stefan A1 - Taffe, A. A1 - Braml, T. ED - Sykora, M. ED - Lenner, R. ED - de Koker, N. T1 - On the Treatment of Measurement Uncertainty in Stochastic Modeling of Basic Variables JF - Acta Polytechnica N2 - The acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based random variables to be included in reliability assessment. It is shown, how measured data representing time-invariant characteristics, in this case non-destructively measured inner geometrical dimensions, can be transferred into measurement results that are both comparable and quality-evaluated. The calculations are based on the rules provided in the guide to the expression of uncertainty in measurement (GUM). The GUM-framework is internationally accepted in metrology and can serve as starting point for the appropriate processing of measured data to be used in assessment. In conclusion, the effects of incorporating the non-destructively measured data into reliability analysis are presented using a prestressed concrete bridge as case-study. T2 - International Probabilistic Workshop 2022 (IPW2022) CY - Stellenbosch, South Africa DA - 09.09.2022 KW - Reliability KW - Assessment KW - Existing structures KW - Measurement uncertainty PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556580 DO - https://doi.org/10.14311/APP.2022.36.0109 SN - 2336-5382 VL - 36 SP - 109 EP - 118 PB - Czech Technical University CY - Prague AN - OPUS4-55658 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -