Bayesian performance assessment of existing concrete structures combining different types of information from inspections and monitoring

Abstract

When assessing existing concrete structures, adequate prediction of the time-dependent structural performance is crucial. Unfortunately, degradation processes are associated with large uncertainties and when executing additional investigations and measurements, significant model and measurement uncertainties play a dominant role in the reliability-based performance prediction. Bayesian updating provides a suitable engineering tool to adequately consider and combine available information for updating prediction models, enabling inferences that are difficult or impossible to make with traditional statistical approaches. Among others, uncertainties on degradation parameters and variables in structural reliability calculations can be updated based on combined information from measurements, monitoring, visual inspections and even quality control. Consequently, these updated uncertainties can be taken into account in full-probabilistic structural reliability calculations or partial factors for the structural verification can be adjusted according to the posterior probabilistic models in order to perform an instantaneous or time-dependent structural assessment. In this work, the Bayesian coupling of different types of information into the assessment process is explained, the predictive power of combined information is illustrated and particular challenges for future research developments are pointed out. Finally, an outlook is given on future engineering challenges to integrate such approaches further in the life-cycle assessment of existing structures. ARTICLE HISTORY