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Increasing requirements on the infrastructure caused by more and heavier traffic coincide with both the aging and the progressive degradation of bridge constructions. These exemplary issues lead to a non-negligible need for reassessments of existing bridges. Compared to the new building design, safety reserves can be used in the evaluation of such existing structures, since uncertainties that may arise during the design, construction and previous Service time are better known or no longer present. Uncertainties can be evaiuated more precisely by measurements. In the meantime, the application of advanced NDT-methods on concrete structures have become so capable, that crucial structure Parameters like certain material properties or geometrical dimensions can be quantified without significant destructive interventions. The measured data can be explicitly incorporated into the probabilistic computation models used for the bridge reassessment in form of random variables. Therewith, a previously insufficient model can be refined to achieve a higher level of approximation. This contribution emphasises two fundamental steps of the measured data based probabilistic reassessment: The sensitivity analyses to identify the cruciai Parameters and the needed accuracy of the related information as well as the comparison with expected uncertainties of non-destructively measured Parameters.
Increasing requirements on the infrastructure caused by more and heavier traffic coincide with both the aging and the progressive degradation of bridge constructions. These exemplary issues lead to a non-negligible need for reassessments of existing bridges. Compared to the new building design, safety reserves can be used in the evaluation of such existing structures, since uncertainties that may arise during the design, construction and previous Service time are better known or no longer present. Uncertainties can be evaiuated more precisely by measurements. In the meantime, the application of advanced NDT-methods on concrete structures have become so capable, that crucial structure Parameters like certain material properties or geometrical dimensions can be quantified without significant destructive interventions. The measured data can be explicitly incorporated into the probabilistic computation models used for the bridge reassessment in form of random variables. Therewith, a previously insufficient model can be refined to achieve a higher level of approximation. This contribution emphasises two fundamental steps of the measured data based probabilistic reassessment: The sensitivity analyses to identify the cruciai Parameters and the needed accuracy of the related information as well as the comparison with expected uncertainties of non-destructively measured Parameters.
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.
Measuring means knowing. The structural engineer’s Knowledge about structures is vitally important for the assessment of their structural safety. This contribution shows, how non-destructive testing methods can be used to collect valuable Information about existing structures. This value is expressed in this paper by the usability in probabilistic assessments and thus by the reliability of the information. The development of non-destructive testing methods in civil-engineering allows the realistic measurement and visualization of inner constructions of concrete components with a minimum of destructive interventions.
The evaluation of the quality of measurement data is of fundamental importance for quantitative measurements in order to ensure the objectivity of testing and evaluation and to assess the reliability of the knowledge acquired. Both systematic and random deviations must be identified, quantified and taken into account to obtain statistically sound data. The Focus of this contribution is on the methodical path, how displayed measurement data can be processed into reliable knowledge.
It is not about developing assessment methods but about providing
necessary knowledge to increase their operational usability.
Measuring means knowing. The structural engineer’s Knowledge about structures is vitally important for the assessment of their structural safety. This contribution shows, how non-destructive testing methods can be used to collect valuable Information about existing structures. This value is expressed in this paper by the usability in probabilistic assessments and thus by the reliability of the information. The development of non-destructive testing methods in civil-engineering allows the realistic measurement and visualization of inner constructions of concrete components with a minimum of destructive interventions.
The evaluation of the quality of measurement data is of fundamental importance for quantitative measurements in order to ensure the objectivity of testing and evaluation and to assess the reliability of the knowledge acquired. Both systematic and random deviations must be identified, quantified and taken into account to obtain statistically sound data. The Focus of this contribution is on the methodical path, how displayed measurement data can be processed into reliable knowledge.
It is not about developing assessment methods but about providing
necessary knowledge to increase their operational usability.