The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The widely adopted assumption of uncorrelated Gaussian error may lead to inaccurate parameter estimation and overconfident predictions, which may lead to suboptimal decisions. This article addresses the challenges of performing Bayesian system identification for structures when large datasets are used, considering both spatial and temporal dependencies in the model uncertainty. We present an approach to efficiently evaluate the log-likelihood function, and we utilize nested sampling to compute the evidence for Bayesian model selection. The approach is first demonstrated on a synthetic case and then applied to a (measured) real-world steel bridge. The results show that the assumption of dependence in the model prediction uncertainties is decisively supported by the data. The proposed developments enable the use of large datasets and accounting for the dependency when performing Bayesian system identification, even when a relatively large number of uncertain parameters is inferred.@en

The modelling of bond behavior can be an important aspect in the reliability assessment of corroded reinforced concrete structures. However, existing models for bond properties of corroded reinforcement - to our knowledge - are not expressed in probabilistic terms and hence not considered in reliability analyses. This paper aims to fill this gap by (i) quantifying the uncertainties associated with a selected model that translates a reinforcing bar area loss to the change in bond-slip properties; and (ii) propagating these uncertainties to structural reliability to assess their importance. The frequentist paradigm is applied for the statistical analysis of experimental data for the bond model of corroded reinforcement proposed in the literature. Reliability assessment of an illustrative example - a simply supported beam with lap splices - is completed with and without considering the uncertainties in this bond model. The reliability calculations concern ultimate limit state verifications, using non-linear finite element analysis. The results show that the neglect of uncertainties in the bond model for corroded reinforcement can lead to about 10 times underestimation of the failure probability, hence their consideration in reliability assessment is advised.@en