Civil infrastructure facilities and structures are normally exposed to a range of severe operating and environmental conditions in the course of their service life. These conditions are the principle causes of deterioration of structural stiffness and stability with time. Failure of certain structures, triggered by either anthropogenic or environmental extreme events, may result in considerable economic and social losses. With this in mind, reliability analysis is a tool commonly used in evaluating and managing structural safety as well as serviceability. This is geared towards providing quantitative information that a selected structure can actually withstand future extreme forces with an appreciable level of reliability in its future service life.
Many researchers have focused on safety evaluation as well as damage assessment of existing aging structures in the past decades. It has been shown that several factors such as load intensity variation with time, environmental conditions, and periodic maintenance have critical impacts on the safety of a structure. Unfortunately, precise impact of these factors are generally challenging to predict. Owing to the presence of time-dependent behavior of load processes as well as structural resistance, service life prediction and safety evaluation should be done implementing reliability-based methods, taking into account the time-dependence and uncertainties related to resistance and load characteristics.
Modelling the resistance deterioration processes is pivotal when it comes to structural reliability analysis. Cao Wang (PhD student jointly supervised by The University of Sydney and Tsinghua University, Quanwang Li at Tsinghua University and Hao Zhang at The University of Sydney developed a novel model for demonstrating the deterioration of aging structures. Deterioration was considered as a combination of two stochastic processes, which are gradual deterioration triggered by environmental effects as well as shock deterioration caused by severe load attacks. They considered the dependency of the deterioration magnitude on the intensity of load. Their work is published in peer-reviewed journal, Reliability Engineering and System Safety.
The authors developed a new deterioration model for aging civil structures. The model had two features. First, the model described non-increasing stochastic process with parameters that had clear physical significance and could be calibrated with observed data. Secondly, it allowed for the correlation between load processes captures and deterioration. The authors presented illustrative examples as well as parametric studies in a bid to assess the sensitivity of structural reliability on variations related with shock and gradual deteriorations, and the load-deterioration correlation.
The research team found that the magnitude as well as variation of the cumulative gradual deterioration had comparable effects on structural reliability. A severer gradual deterioration associated with the gradual deterioration resulted in a large failure probability of the structure as well as shorter service life. The authors found that ignoring the gradual deterioration variation would have underestimated the structural failure probability.
The structural reliability was sensitive to magnitude of shock deterioration. However, the variation of shock deterioration had minor effects on structural reliability. Hence, a researcher may employ an empirical estimate of the variance of shock deterioration in practical reliability analysis. In instances where the uncertainties of deteriorations as well as loads were comparable, a larger load-deterioration correlation led to more failure probability of the structure. Ignoring the correlation between load processes and deterioration would have overestimated the structural reliability as well as service life.
Cao Wang, Hao Zhang, Quanwang Li. Reliability assessment of aging structures subjected to gradual and shock deteriorations. Reliability Engineering and System Safety, volume 161 (2017), pages 78–86.Go To Reliability Engineering and System Safety