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Performance indicator of a bridge expansion joint

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Author: Kalkman, I.M. · Lentzen, S.S.K. · Courage, W.M.G. · Napoles, O.M. · Galanti, F.M.B.
Source:3rd International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2011, 25 May 2011 through 28 May 2011, Corfu. Conference code: 86982
Identifier: 442995
Keywords: Architechture · Damage prediction · Expansion joint · Failure parameters · Inverse modeling · Maintenance planning · Structural health monitoring · Damage prediction · Failure parameters · Inverse modeling · Maintenance planning · Structural health · Benchmarking · Civil engineering · Computational methods · Damage detection · Degradation · Earthquakes · Engineering geology · Expansion · Expansion joints · Finite element method · Forecasting · Health risks · Inverse problems · Maintenance · Management · Managers · Markov processes · Mathematical models · Safety engineering · Structural dynamics · Monitoring · Building Engineering & Civil Engineering · SD - Structural Dynamics SR - Structural Reliability · TS - Technical Sciences


In general the condition of a structure can be assessed in terms of a performance indicator. For example, this can be the strength of a structure. An asset manager is concerned with ensuring that the performance of a structure does not fall below a given minimum level. This can be achieved by inspecting or monitoring the structure. As the performance indicator decreases with time, the asset manager can decide to take pre-emptive measures to restore the condition to its initial level, thus avoiding getting too close to the minimum required level. In order to work this way, it is important to define a reliable performance indicator. Following an inventory of structures which are prone to some form of degradation over time, a modular bridge expansion joint was selected as a case to be considered in this investigation. In order to determine the observability of known failure mechanisms in terms of modal and spectral data an experiment is set up which can simulate the construction under these circumstances. Further, a Finite Element Model of the construction is made, which is tuned to the experimental setup. This model is used to validate the applied inverse modeling technique to identify the failure parameters. The inverse modeling is performed using a genetic algorithm. Through inverse modeling of the monitor data, changes over time of the identified failure parameters are obtained. In order to predict the development of the observed failure parameters in the future, these changes over time are incorporated in a prediction model. Taking uncertainties into account, stochastic processes are used to describe the degradation process. Thus, different types of processes can be used, e.g. Markov chains for discrete state changes in time or Gamma processes for continuous quantities in time. By continuously updating the prediction model with the monitor data, a risk based maintenance management tool is obtained by which pro-active and well-planned maintenance actions can be decided on. The developed methodology is applied to a full scale monitoring system of a real bridge in the Netherlands.