This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge's displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis-Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods.

This paper presents a feature extraction method to uncover the temperature effects on bridge responses, 5 which combines mode decomposition, data reduction and blind separation. For mode decomposition, 6 empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) have 7 been selected, followed by principal component analysis (PCA) for data size compression. The 8 independent component analysis (ICA) is then employed for blind separation. The unique feature of the 9 proposed method is the blind separation, which means temperature-induced response can be extracted 10 from the mixed structural responses, without any prior information of the loading conditions and 11 structural physical models. This study further evaluates the effects of extracting temperature-induced 12 response on damage detectability when using Moving Principal Component Analysis (MPCA). The 13 numerical analysis of a truss bridge is first used to evaluate the proposed method for thermal feature 14 extraction, followed by a real truss bridge test in the structural laboratory in University of Warwick. 15 Results from the numerical case study show that the method enables the separation of temperature-16 induced response, and furthermore, the EEMD, in mode decomposition, has a positive influence on the 17 blind separation than EMD, when combined with PCA and ICA. Finally, the real truss bridge test 18 demonstrates that the feature extraction method can enhance the probability of MPCA to uncover the 19 damage, as the MPCA fails without proposed method. 20

A statistical analysis of the dynamic response of a railway viaduct, modelled after an actual structure, is presented. The finite element model of the viaduct is based on the data provided by the Portuguese Railway Company REFER EPE. The train load is simplified by a set of constant moving forces and the range of velocities implemented corresponds to typical velocities of circulation. The viaduct is composed of eight modules, but, for the sake of simplicity, only the first viaduct module is included in the analysis.In order to perform the statistical analysis, the viaduct is subjected to a two-level factorial design. It is shown that key parameters cannot be analysed individually because in some cases interaction effects can be more important than single effects.Response functions of significant results are presented. Their usage for dynamic response estimates is exemplified. Further it is shown how they can be used for the determination of a probability that a certain value of interest is exceeded, provided the range of key parameters corresponds to the interval of uncertainties, where the true value obeys the normal distribution.This type of straightforward application of statistical analysis highlights the interaction of adequately selected key parameters, provides useful information for design guidelines and is believed to lead to better planning and more realistic representation of the actual response of railway bridges.

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