Abstract
Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.
Highlights
Prediction of structural deterioration has intrinsic difficulties originating from erroneous assumptions in the deterioration progress model and uncertainties, and temporal changes in environmental conditions
A total of 5 sets of measurements until operational year 22 assimilated with the stochastic corrosion propagation model using the particle filter framework
Particle filter approach is used as a spatiotemporal evolution of structural deterioration
Summary
Prediction of structural deterioration has intrinsic difficulties originating from erroneous assumptions in the deterioration progress model and uncertainties, and temporal changes in environmental conditions. To alleviate errors in the prediction, measurement data could be used to adjust the predicted states or improve the models used for predictions. Such data can be obtained from real-time monitoring of the structure or periodic field inspections. These measurement data may contain various degrees of signal noises and often deterioration states of a structure are not directly measurable due to physical, operational or technical restrictions. There is a pressing need for a probabilistic framework to assimilate successive measurements and numerical deterioration progress models [1,2,3,4]
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