On the treatment of measurement uncertainty in stochastic modeling of basic variables

Authors

  • Stefan Küttenbaum Bundesanstalt für Materialforschung und -prüfung (BAM), Division 8.2: Non-Destructive Testing Methods for Civil Engineering, Unter den Eichen 87, 12205 Berlin, Germany
  • Stefan Maack Bundesanstalt für Materialforschung und -prüfung (BAM), Division 8.2: Non-Destructive Testing Methods for Civil Engineering, Unter den Eichen 87, 12205 Berlin, Germany
  • Alexander Taffe HTW Berlin (UAS), Division Building materials sciences and diagnostics, 12459 Berlin, Germany
  • Thomas Braml Universität der Bundeswehr München, Inst. of Struct. Engineering, 85577 Neubiberg, Germany

DOI:

https://doi.org/10.14311/APP.2022.36.0109

Keywords:

existing structures, FORM, measurement uncertainty, nondestructive testing, reliability assessment

Abstract

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.

Downloads

Published

2022-08-18