Stochastic Life-cycle Analysis of Deteriorating Infrastructure Systems and an Application to Reinforced Concrete Bridges

Infrastructure systems are critical to a country?s prosperity. It is extremely important to manage the infrastructure systems efficiently in order to avoid wastage and to maximize benefits. Deterioration of infrastructure systems is one of the primary issues in civil engineering today. This problem has been widely acknowledged by engineering community in numerous studies. We need to evolve efficient strategies to tackle the problem of infrastructure deterioration and to efficiently operate infrastructure.

In this research, we propose stochastic models to predict the process of deterioration in engineering systems and to perform life-cycle analysis (LCA) of deteriorating engineering systems. LCA has been recognized, over the years, as a highly informative tool for helping the decision making process in infrastructure management. In this research, we propose a stochastic model, SSA, to accurately predict the effect of deterioration processes in engineering systems. The SSA model addresses some of the important and ignored areas in the existing models such as the effect of deterioration on both capacity and demands of systems and accounting for different types of failures in assessing the life-span of a deteriorating system. Furthermore, this research proposes RTLCA, a renewal theory based LCA model, to predict the life-cycle performance of deteriorating systems taking into account not only the life-time reliability but also the costs associated with operating a system. In addition, this research investigates the effect of seismic degradation on the reliability of reinforced concrete (RC) bridges. For this purpose, we model the seismic degradation process in the RC bridge columns which are the primary lateral load resisting system in a bridge. Thereafter, the RTLCA model along with SSA model is used to study the life-cycle of an example RC bridge located in seismic regions accounting for seismic degradation. It is expected that the models proposed in this research will be helpful in better managing our infrastructure systems.