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Monitoring 4.0/5.0
(2021)
Mehr als 10 % des deutschen Bruttoinlandsprodukts werden für Investitionen imBausektor eingesetzt. Die unmittelbar folgende Erkenntnis, dass eine zukunftsorientierte, nachhaltige Umgestaltung unserer Gesellschaft nicht ohne einen substanziellen Beitrag des Bausektors gelingen kann, hat sich mittlerweile weitgehend durchgesetzt. Die Komplexität der zugehörigen technischen Herausforderungen ist jedoch nicht zu unterschätzen.
This specification describes how corrosion monitoring is used to check the condition of reinforced and prestressed concrete members. In this specification, the term "corrosion monitoring" covers all methods in which it is possible to continuously track corrosion-relevant variables in the area of stationary, built-in sensors. This specification does not cover other possible methods, such as potential field measurements, involving the use of mobile, portable sensors that are placed temporarily on the membersurface to take measurements.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the
probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.