This thesis estimates the impact of visual inspection prior to its implementation in a Bridge Management System (BMS) using Value of Information (VoI). Visual inspection is the principal assessment method for bridge structures, whereby a condition rating is assigned reflecting the structural condition of a bridge, based on the judgements of a trained inspector. The impact of data collected from visual inspection is contingent on its ability to guide towards optimal maintenance decisions throughout the lifecycle to maximise network performance. The VoI concept from Bayesian pre- posterior analysis is defined as the quantification of the reduction of uncertainty in a decision-making problem, after new information is received. This concept has seen multifaceted applications in the optimisation of Structural Health Monitoring techniques, typically focussing on the ability to monitor a specific parameter to determine the degradation rate and condition of a single asset. The merits of visual inspection data have been largely overlooked thus far. This work outlines and applies a framework to put a measure on the impact that visual inspection provides to infrastructure asset managers operating a BMS, and to illustrate how this is influenced by the underlying uncertainties of the model parameters.