One of the most severe deteriorations in reinforced concrete structures is associated with reinforcement corrosion, where the corrosion distribution shows considerable spatial variability. In the existing investigations of spatial variability of corrosion on the reliability of reinforced concrete structures, symbolic expressions are used for the structural performance. However, structural behaviors like stress redistribution and plasticity spread are not captured. In this research, a computational framework is designed to couple the probabilistic analysis with the nonlinear finite element analysis. Random fields is used with the computational framework to represent the spatial variability of corrosion.A 60 m girder of a reinforced concrete bridge is taken as the case study to explore the effect of spatial variability of corrosion on reliability of static indeterminate reinforced concrete structures. The reliability analysis is target on ultimate capacity of the beam to carry the traffic load. The girder is modeled as a 3 span continuous beam. The modelling of corrosion damages on reinforcement (area of cross section and physical properties) is based on assumption of pure pitting corrosion. In the axial direction of the beam, spatial variability is modeled with random field. In order to study the effect of different level of spatial variability, the correlation length of the random field is set from infinite large to 125 mm. Between adjacent bars, extreme cases of fully spatial correlated and totally spatial independent are studied.The case study shows:i) when corrosion develops, pitting corrosion can severely reduce resistance of the structure and localized damage may lead to a brittle structural response.ii) spatial variability of pitting corrosion in the axial direction of the beam leads to higher failure probability of the structure.iii) spatial variability of pitting corrosion between adjacent bars leads to lower failure probability of the structure.iv) the effect of spatial variability of corrosion on reliability of static indeterminate reinforced concrete structure is a collective effect of probabilistic and physical characteristic.In order to facilitate the reliability assessment of corroded reinforced concrete structures, additional efforts are also contributed to: quantification of uncertainty for the bond model of corroded reinforcement; comparison of various reliability methods with the probabilistic nonlinear finite element analysis framework.The thesis fills the knowledge gap of probabilistic nonlinear finite element analysis of reinforced concrete structures with spatial varied corrosion and quantifies the effects of spatial variability of corrosion on the reliability of a static indeterminate reinforced concrete structure. The analysis framework designed in this thesis is also applicable for other reliability assessment of corroded reinforced concrete structures.

Bayesian system identification has been extensively adopted in Structural Health Monitoring as a way to probabilistically infer unobservable parameters of the physical model of a structure using measurement data. Combining the Bayesian approach with distributed optic fibre sensors can potentially improve the accuracy and reduce the uncertainty in parameter estimation problems, given the large amount of quasi-continuous data produced by this sensing technology; however, its computational cost could be prohibitively high when using conventional methods since Bayesian inference typically involves a large number of samples, each of which entails a physical model evaluation. The focus of this work is on performing Bayesian system identification for real-world civil engineering structures within an acceptable running time, while using optic fibre measurements with a high spatial resolution. The proposed methodology employs a cheap-to-compute Gaussian process (GP) surrogate that replaces the main bottleneck of the Bayesian workflow for these type of problems: the evaluation of the log-likelihood. The GP surrogate is actively built by sequentially selecting new training points in areas that are expected to highly contribute to the accuracy of the posterior distribution. Once convergence is achieved, the surrogate is used to obtain the parameter estimates via Markov chain Monte Carlo (MCMC) sampling. Additionally, in order to accelerate the Bayesian workflow, cloud-based parallelization is used to perform multiple finite element analyses simultaneously. A first synthetic case with an inexpensive frame model is used to test the methodology for problems with two and five probabilistic parameters. An encouraging outcome is obtained with the actively learned GP surrogate, with posterior distributions very close to the full MCMC procedure while requiring a number of physical model evaluations orders of magnitude lower.  After that, a second case study consisting of an existing reinforced concrete bridge with real measurements and a relatively expensive finite element model is investigated. A subset of discrete strain and translation sensors are used to perform an initial parameter estimation that almost exactly resembles the results from previous research on this bridge by Rózsás, et al. (2022), successfully validating the proposed procedure. Then, another parameter estimation is computed using available high resolution optic fibre measurements, after which is shown that the optic fibre provides the best improvements in model predictive capacity among all sensor groups, confirming its potential when combined with Bayesian system identification. The results of the case studies indicate that the approach presented in this thesis has the capacity to greatly reduce the wall-clock time of Bayesian parameter estimation for real world civil engineering structures with optic fibre measurements, while maintaining a high degree of accuracy. Nevertheless, additional research is required for cases where the statistical parameters governing the measurement and model uncertainty are inferred along with the physical parameters.