Life-cycle modelling of concrete cracking and reinforcement corrosion in concrete bridges: A case study

The development of effective life cycle management strategies for transport infrastructure assets is of importance for meeting the defined public policies and levels of service. In the last decades, much progress has been made in assessing the life-cycle performance of bridges using reliability-based approaches. However, the goal of developing a comprehensive life-cycle performance assessment framework for bridges has not been fully achieved. This is due to the uncertainties surrounding model parameters as well as the correlation between these parameters (e.g. the complex correlation between the reinforcement corrosion and the concrete cracking). It becomes more challenging due to the limited access to bridge inspection data by bridge research communities resulting from confidentiality issues. Using a typical highway concrete bridge as a case study, the present study systematically investigated the impact of concrete crack induced reinforcement corrosion on the serviceability of concrete bridges by developing an engineering reliability-based approach involving an auto-regressive crack propagation model and a steel corrosion prediction model. The model parameters were calibrated using the eight-year inspection data of an operating bridge. The influence of different external environments in the reinforcement corrosion, ultimately the residual life of the bridges, was also investigated through conducting a series of parametric studies. Based on the collected bridge inspection data, the model results predict that, although the surface crack of a RC bridge is repairable through periodic maintenance, the corrosion of the steel bars in the bridge still continues over time with a corrosion rate which depends on different maintenance intervention cycle periods (Tcycle). For example, reducing Tcycle from 12 years to 4 years could potentially prolong the service life of the bridge by around 15 years. The developed model could assist bridge managers to estimate the optimal Tcycle to prolong the service life of bridges