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A major advantage in the reassessment of existing structures is the possibility of including measured data that describe the actual properties and the current condition of the structure to be reassessed. Currently, the incorporation of such measured information is mostly unregulated. However, the use of measurement results is vitally important, since a measured data-based improvement of the computation models level of approximation can lead at least to more meaningful results, possibly to extended remaining life times of the structure and in the best case to a saving of resources. Conversely, not appreciating well measurable and relevant information can be equated with a waste of resources. In this paper, a concept for the comparable use of non-destructively measured data as basic variables in probabilistic reliability assessments is outlined and examined using a typical prestressed concrete road bridge as a case-study. An essential requirement is the calculation of measurement uncertainties in order to evaluate the quality of the measurement results comparably. In conclusion, the example of ultrasonic and radar measurement data is used to demonstrate the effects that the incorporation of the measured information has on the reliability of the structure.
An interlaboratory comparison (ILC)was organised to characterise 87Sr/86Sr isotope ratios in geological and industrial reference materials by applying the so-called conventional method for determining 87Sr/86Sr isotope ratios. Four cements (VDZ 100a,VDZ 200a, VDZ 300a, IAG OPC-1), one limestone (IAG CGL ML-3) and one slate (IAG OU-6) reference materials were selected, covering a wide range of naturally occurring Sr isotopic signatures. Thirteen laboratories received aliquots of these six reference materials together with a detailed technical protocol. The consensus values for the six reference materials and their associated measurement uncertainties were obtained by applying a Gaussian, linear mixed effects model fitted to all the measurement results. By combining the consensus values and their uncertainties with an uncertainty contribution for potential heterogeneity, reference values ranging from 0.708134 mol mol-1 to 0.729778 mol mol-1 were obtained with relative expanded uncertainties of ≤ 0.007 %. This study represents an ILC on conventional 87Sr/86Sr isotope ratios, within which metrological principles were considered and the compatibility of measurement results obtained by MC-ICP-MS and by MC-TIMS is demonstrated. The materials characterised in this study can be used as reference materials for validation and quality control purposes and to estimate measurement uncertainties in conventional 87Sr/86Sr isotope ratio measurement.
Isotope ratio applications are on the increase and a major part of which are delta measurements, because they are easier to perform than the determination of absolute isotope ratios while offering lower measurement uncertainties. Delta measurements use artefact-based scales and therefore scale conversions are required due to the lack of the scale defining standards. Such scale conversions often form the basis for comparing data being generated in numerous projects andtherefore need to be as accurate as possible. In practice, users are tempted to apply linear approximations, which are not sufficiently exact, because delta values are defined by nonlinear relationships. The bias of such approximations often is beyond typical measurement uncertainties and its extent can hardly be predicted. Therefore, exact calculations are advised. Here, the exact equations and the bias of the approximations are presented, and calculations are illustrated by real-world examples. Measurement uncertainty is indispensable in this context and therefore, its calculation is described as well for determining delta values but also for scale conversions. Approaches for obtaining a single delta measurement and for repeated measurements are presented. For the latter case, a new approach for calculating the measurement uncertainty is presented, which considers covariances between the isotope ratios.
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.
Laboratory test results are of vital importance for correctly classifying and labelling chemicals as “hazardous” as defined in the UN Globally Harmonized System (GHS) / EC CLP Regulation or as “dangerous goods” as defined in the UN Recommendations on the Transport of Dangerous Goods. Interlaboratory tests play a decisive role in assessing the reliability of laboratory test results. Interlaboratory tests performed over the last 10 years have examined different laboratory test methods. After analysing the results of these interlaboratory tests, the following conclusions can be drawn:
1. There is a need for improvement and validation for all laboratory test methods examined.
2. To avoid any discrepancy concerning the classification and labelling of chemicals, the use of validated laboratory test methods should be state of the art, with the results accompanied by the measurement uncertainty and (if applicable) the probability of incorrect classification.
This paper addresses the probability of correct/incorrect classification (for example, as dangerous goods) on the basis of the measurement deviation obtained from interlaboratory tests performed by the Centre for quality assurance for testing of dangerous goods and hazardous substances (CEQAT-DGHS) to validate laboratory test methods. This paper outlines typical results (e.g. so-called “Shark profiles” – the probability of incorrect classification as a function of the true value estimated from interlaboratory test data) as well as general conclusions and steps to be taken to guarantee that laboratory test results are fit for purpose and of high quality.