This research focuses on reliability-based, Bayesian updating of grout anchors’ bearing capacity. Firstly, grouted anchors are commonly used in practice, even though large uncertainties surround their bearing capacity. Secondly, every anchor must be tested on their working load for quality assurance. Because of the combination of those two factors, Bayesian updating on grout anchors yields large potential. The measurement data can be used in the design process, to reduce uncertainties. The main goal of this research was to establish how reliability-based updating can be applied in the context of grout anchor bearing capacity. The motivation to use such techniques is to increase the efficiency of existing structures, like in this case study for quay walls, or to adapt the design during construction, effectively cutting costs in both cases. This research was conducted using measurement data from the HHTT quay wall at the port of Rotterdam, applying a recently developed analytical anchor displacement model, and the Bayesian updating technique ’aBUS-SuS’. The combination of both of these methods in this context is a novelty approach to the subject. The first step was to derive the soil parameters necessary to reproduce the measurements to prove firstly, that the model is capable to capture anchor behavior, secondly to get a better understanding of how the model functions in this context, and thirdly to establish a baseline of the parameters necessary for the estimation of prior distributions. The second step was the application of the Bayesian updating algorithm. There, the prior distributions, which are needed as an input, are generated using the results from the first step. The resulting posterior distributions are then related to failure limits of the critical soil parameters that were derived according to a common anchor failure criterion. This gives an indication of the anchor’s utilization and disposition to failure. The results exhibited failure conditions for those anchors that were at failure, verifying that the novelty approach can be applied to the problem. Another finding was that depending on the soil parameter, conclusions can be drawn about the likeliness of the failure type. This means how brittle or elastic the failure might be. The analyses showed that the studied procedure has potential and the anchor’s proneness to failure can be assessed. The underlying analytical model is suitable for the applied type of Bayesian updating because the results are in the same range as for the calibration, but on average slightly lower in their mean magnitude. Thus, additional information is obtained, and the overall uncertainty is reduced. Furthermore, the analyses of the suitability tests gave a quantifiable indication of the anchors’ reliability under failure load that was in line with measured failure indication under working load. Even though the uncertainty that surrounds the anchor-design was reduced, limitations to the framework arose. The analytical model requires uncommon soil parameters as an input. Those parameters need to be specifically determined in laboratory investigations and cannot be tested for on site. The model in- and output needs to match the measurements to the failure criterion of the anchors, and in anchor design, advanced analytical models, like the one used in this research are scarce. The measurement always have certain errors inherent. These errors can lead to diffuse and noisy posterior distributions. Furthermore, a minimum amount of measurements must be available in the first place, otherwise the algorithm does not converge properly towards a solution. This is why it is recommended to get detailed insight of the soil parameters at question also under higher stresses and strains than the anticipated working loads. This helps to give better estimations of the prior distributions and their bounds, and also gives larger confidence in the posterior distributions as a result. Furthermore, this also helps to establish precise parametric limits corresponding to failure, for which the reliability can be evaluated.