A two-phase model to predict the remaining useful life of corroded reinforced concrete beams

A two-phase modeling and estimation framework to structural health monitoring of infrastructure components is presented in this paper. The main contribution of this paper is to present a mathematical and implementation framework to detect failures and to predict the remaining useful life in long-life infrastructure components (e.g., bridge components), where actual failure data may be unavailable. A hybrid approach where laboratory data are fused with in-situ measurements is proposed to overcome the difficultly of data unavailability. Taking into account practical constraints related to field measurements, surrogate measures are used as an indirect means to model the mechanism of degradation in the component(s) of interest. The model proposed for degradation is a two-phase gamma process which can handle cases where an event causes a change in the rate of degradation (e.g., stiffness loss). A Bayesian approach is used to estimate the model parameters and the residual useful life. The key step of determining the priors is accomplished using the laboratory test data, which is subsequently integrated with data from a monitored specimen for posterior inference. This hybridization overcomes some of the difficulties associated with the lack of failure data in long-life components, typical of civil infrastructure. The methodology is demonstrated using measurement data acquired from near full-scale laboratory corroded reinforced concrete beam specimens. For the sake of completeness, the results are compared with the single-phase degradation modeling approach, and the practical advantages of using a two-phase model are underscored.