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"The reassessment of bridges continues to take great importance both nationally and internationally. A major challenge is to find computation models reflecting the actual properties of the considered structures sufficiently accurate. Besides regular inspections, the conduction of advanced measurements is suitable to generate reliable information about a structure to be assessed. Prior to incorporating measurement results in reassessment, the relevance, the trueness, and the precision of the measured information needs to be stated. On the one hand, the use of information whose quality has not been assessed can lead to errors with serious consequences. On the other, the measurement of irrelevant information is inefficient. Although the use of measured data in assessment is currently mostly unregulated, their appreciation in reliability analyses is beneficial since the built environment can be assessed more realistically. Utilizing NDT in reassessment has the potential to extend remaining lifetimes of a structure, save resources, and improve infrastructural availabilities. The power of judgment regarding the decision on the reliability of an existing structure can be increased.
In this contribution, an approach is outlined to process non-destructively gathered measurement data in a comparableway in order to include themeasured information in probabilistic reliability assessments of existing structures. An essential part is the calculation of measurement uncertainties. The effect of incorporating evaluated NDT-results is demonstrated by means of a prestressed concrete bridge and GPR measurements conducted on this bridge as a case-study. The bridge is assessed regarding SLS Decompression using the NDT-results."
Corrosion of concrete reinforcement is one of the major damage mechanisms affecting both the load-bearing capacity and the serviceability of re-inforced concrete structures significantly. When externally discernible damages are observed during visual inspections on the structure, the extent of the damage inside the concrete is often already significant. Corrosion caused by carbonation often leads to severe discoloration of the surface or even large-area spalling of the concrete cover. In contrast, chloride-induced corrosion is usually difficult to observe visually but can cause much more serious damage in less time. The effect occurs locally and can lead to weakening of the cross-section of the reinforce-ment. This, in turn, can cause sudden structural collapses without prior notice.
In the meanwhile, various non-destructive and minimally invasive testing methods are available to evaluate the resistance to penetration of corrosion-pro-moting pollutants and to detect active corrosion. In this paper, a bridge crossing the river Regen is used as a case-study to demonstrate how the information ob-tained applying different testing methods can be combined and evaluated in the context of structural reassessments. Both the results of the permeability testing and the electrical resistance measurement are considered, as well as active corro-sion areas are localized using the half-cell potential mapping combined with the concrete cover measurement with the eddy current method and ground penetrat-ing radar. The results are evaluated using drill cores and in addition laser-induced breakdown spectroscopy was applied to obtain information about possible chlo-ride ion transport into the concrete.
The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspacebased residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of Output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study
and on experimental data of a progressively damaged steel frame.
The critical chloride content C(crit) is an indicator for the corrosion resistance of reinforcing steels in concrete. In this article, several experimental methods for determining the critical chloride content C(crit) of mild and an alloyed (12% Cr) steel in cementitious materials are compared. The methods used include accelerated polarization tests and tests using more natural (unpolarized) conditions in which chloride ingress occurs by diffusion and capillary uptake. The advantages and disadvantages of the methods are discussed, in particular, with the objective of reaching a compromise between applicability to practice and feasibility in laboratory testing.
A method to determine the influencing parameters of a structural and damage detection system is proposed based on the value of Information analysis. The value of information analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of damage detection system for the structural integrity management during service life. First, the influencing parameters of the structural system, such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the influencing parameters on the damage detection system performance, including number of sensors, sensor locations, measurement noise, and the Type-I error are investigated. The preposterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of damage detection system is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences, as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the deterioration rate is the most sensitive parameter on the effect of relative value of information over the whole service life. Furthermore, it shows that more sensors do not necessarily lead to a higher relative value of information; only specific sensor locations near the highest utilized components lead to a high relative value of information; measurement noise and the Type-I error should be controlled and be as small as possible. An optimal sensor employment with highest relative value of information is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies—before implementation—for similar bridges and to optimize the damage detection system settings and sensor configuration for minimum expected Costs and risks.