Navigational locks are critical infrastructure components; however, traditional inspection of the lock components, requires closing and dewatering the lock to perform visual inspections, which stops fluvial traffic and consequently presents significant economic losses. Moreover, the gates are typically the critical component in the navigational locks; due to impact or environmental deterioration, a gap occurs in the quoin between the gate and the wall, then the gap produces redistribution of the stresses that are rarely considered during the design of the gates, and this modification in the response accelerate the deterioration in some components of the gate. To overcome this challenge, previous researchers have presented structural health monitoring strategies for miter gates, for example, automated damage detection using strain gage measurements based on principal component analysis have been proposed. In this study, Bayesian model updating of miter gates with uncertain boundary conditions is presented with the following details: the uncertain boundary conditions are represented by the gap profile in the boundary with random depths at discrete locations, the slope of the strain gage measurements with respect to water levels is used as a representative damage feature, and a detailed finite element in Abaqus of the gate and the support is considered, where the gaps are modeled as nonlinear gap elements. Due to the large computational time required for the finite element model used, transitional Markov chain Monte Carlo simulation is implemented to sample the model parameters in high probability regions, from which statistical estimators are obtained. The results demonstrate the potential of the approach to perform Bayesian-based damage prognosis using digital twins.