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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.
Making optimal decisions about the reliability of existing structures requires that the information used in assessment adequately represents the properties and the condition of the structures. The knowledge gap regarding a structure to be assessed can be successively filled by individually purposeful observations on site. This paper gives an overview of an approach for utilizing nondestructively gathered measurement results in reliability assessment of existing structures. An essential part of measurement-based stochastic modeling of basic variables is the calculation of measurement uncertainties, which serves to establish confidence in measurement, to ensure the comparability of unambiguously expressed measurement results, and to quantify the quality of the measured information. Regarding the current discourse on how to treat information collected on-site in the context of assessment, the authors recommend that measurement uncertainty becomes an uncertainty component mandatorily to be represented in measurement-based stochastic models. The main steps of the proposed concept are presented, and the advantages of its application are emphasized by means of a prestressed concrete bridge as case study. The bridge is assessed regarding the serviceability limit state decompression using ultrasonic and radar data measured at the structure.