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

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

Methods to Assess the Quality of Non- Destructive Testing in Civil Engineering Using POD and GUM for Static Calculations of Existing Structures

Abstract. To draw reliable conclusions from results gained with non-destructive testing methods in civil-engineering (NDT-CE) it is important to know about the quality of results. Since recent times, the POD (probability of detection) according to MIL-HDBK-1823A and Berens Report is established in non-destructive testing in civil engineering (NDT-CE) to assess the reliability of qualitative testing problems. For determining the uncertainty of measurements of quantitative (metric) problems the Guide (Guide to the Expression of Uncertainty in Measurements -GUM) is used in NDT-CE. Now, a new approach is to adapt both methods to get statistically secured measurement results with NDT-CE from existing structures such as prestressed bridges. This paper introduces how calculations based on stochastic models can be used together with NDT-CE results analysed with POD and the Guide

Condition assessment: From good choice of methods to reliable results that meet the customer demand

Condition assessment of structures reveals information of the inner structure and its condition. To be insightful the testing task has to be defined regarding the target state defined by the customer. Questions such as “What method should be applied?”, “Where should be measured at what time?” and “How many measurements are sufficient?” have to be answered to guarantee reliable results. Often it is obvious that the additional use of non-destructive methods makes sense. But what method is the most appropriate and what strategy should be applied? This contribution will help to develop a strategy to achieve reliable results. Reliable results have to be accurate which means that reliable results have to be true and precise. According to the GUM (Guideline to the expression of Uncertainty in Measurement) the procedure how to evaluate data statistically will be demonstrated. Furthermore, it will be shown how statistically evaluated data can be used in static calculations to furnish the proof of the stability of the real as-built structure. This profoundly use of data contributes to make good and reliable decisions

Detection of near-surface reinforcement in concrete components with ultrasound

Ultrasonic testing of concrete has grown in importance considerably in recent years in non-destructive testing in civil engineering (NDT-CE). In the past, the main focus was on the imaging of the internal construction of steel and prestressed concrete components. On the other hand, comparatively little attention was paid to the location of near-surface reinforcement and concrete cover measurement. In this research, it is shown to what extent ultrasound is suitable for the detection of near-surface reinforcement in addition to magnetic inductive methods. The measurements were carried out with the newly developed Pundit 250 Array from the company Proceq and with the measuring devices of the company Acsys, the A1220 Monolith and the A1040 Mira. The ultrasound data was analysed with the vendor-independent software InterSAFT of the University of Kassel. Systematic investigations were carried out on test specimens with a variety on the concrete cover, the diameter of the reinforcement and the reinforcement ratio in the form of mesh reinforcement close to the surface. The detectability and accuracy of the concrete cover were set in relation to the concrete cover, wavelength and reinforcement diameter, with the result that more detailed rules for the detection of reinforcement are formulated for the user, instead of the known λ/2-criterion

Approach to the development of a model to quantify the quality of tendon localization in concrete using ultrasound

Each engineering decision is based on a number of more or less accurate information. In assessment of existing structures, additional relevant information collected with on-site inspections facilitate better decisions. However, observed data basically represents the physical characteristic of interest with an uncertainty. This uncertainty is a measure of the inspection quality and can be quantified by expressing the measurement uncertainty. The internationally accepted rules for calculating measurement uncertainty are well established and can be applied straightforwardly in many practical cases. Nevertheless, the calculations require the occasionally time-consuming development of an individually suitable measurement model. This contribution attempts to emphasize proposals for modelling the non-destructive depth measurement of tendons in concrete using the ultrasonic echo technique. The proposed model can serve as guideline for the determination of the quality of the measured information in future comparable inspection scenarios

Reliability assessment of existing bridge constructions based on results of non-destructive testing

The non-destructive testing methods available for civil engineering (NDT-CE) enable the measurements of quantitative parameters, which realistically describe the characteristics of existing buildings. In the past, methods for quality evaluation and concepts for validation expanded into NDT-CE to improve the objectivity of measured data. Thereby, a metrological foundation was developed to collect statistically sound and structurally relevant information about the inner construction of structures without destructive interventions. More recently, the demand for recalculations of structural safety was identified. This paper summarizes a basic research study on structural analyses of bridges in combination with NDT. The aim is to use measurement data of nondestructive testing methods as stochastic quantities in static calculations. Therefore, a methodical interface between the guide to the expression of uncertainty in measurement and probabilistic approximation procedures (e.g. FORM) has been proven to be suitable. The motivation is to relate the scientific approach of the structural analysis with real information coming from existing structures and not with those found in the literature. A case study about the probabilistic bending proof of a reinforced concrete bridge with statistically verified data from ultrasonic measurements shows that the measuring results fulfil the requirements concerning precision, trueness, objectivity and reliability