Abstract Summary
Over the past fifty years, external post tensioning tendons have been extensively used for the design and retrofitting of concrete bridges. Despite its multiple advantages (such as economical construction, ease of installation, re stressing and substitution in case of failure), the behavior of these post tensioning systems is sensitive to both the deterioration and damage caused by three main factors: (i) the increasing traffic loads; (ii) the exposure to adverse environmental conditions (the tendon corrosion is a current significant problem); and (iii) the structural aging. In order to guarantee the adequate performance of these post tensioning systems during the overall life cycle of the bridge, their fatigue assessment must be checked. One key aspect to be considered for an accurate prediction of these external post tensioning systems' fatigue behaviour is quantifying the uncertainties associated with the variation of their modal properties and the increasing traffic loads. In order to compute numerically these uncertainties, information extracted from structural performance monitoring (SPM) can be used. In particular, modal data can be obtained from continuous long term monitoring. Thus, these data (time series) can be considered to perform a Bayesian finite element model (FEM) updating of the structure. As a result of this updating process, the main physical parameters of the post tensioning system can be estimated as random variables. Thus, data obtained from SPM allow for predicting the structural capacity of the tendons accurately. Additionally, information extracted via SPM can be used to analyze the uncertainty associated with the increasing traffic load (the demand). Finally, a reliability fatigue analysis, based on the experimentally estimated capacity and demand, can be employed to compute the expected life cycle of the bridge. In this study, a fatigue reliability analysis of the post tension system of a high speed train bridge has been performed in detail. For this purpose, long term monitoring of the system has been implemented. As a result of this dynamic monitoring, both modal data and information about the real traffic have been extracted. On the one hand, these modal data have been used to perform a Bayesian FEM updating. A Markov Chain Monte Carlo method has been considered to sample the probabilistic functions (normal distribution) which characterize the random behavior of the main physical parameters of the post tensioning system. On the other hand, the effect of traffic loads has been modelled as a random process. Data extracted via SPM has been considered to characterize this process. Additionally, the S N curves (assuming a Weibull distribution to describe the uncertainty of the fatigue strength) and the Palmgren Miner hypothesis were chosen for the definition of the fatigue strength of the considered post tensioning system. Thus, a limit state function for the fatigue assessment can be defined. Finally, the variability of the random variable damage can be computed from this limit state. As a conclusion, this manuscript illustrates how the information extracted via SPM allows quantifying the uncertainty associated with the post tensioning system, and consequently how these data allow computing more accurately
