The ultimate goal of a Structural Health Monitoring and Prognostics system (SHM/DP) is to interrogate the data gathered in situ to infer the state of the structure (diagnostics), evaluate the system's evolution over time (prognos-tics), and leverage the diagnostic and prognostic results to facilitate decision-making. As a compelling business proposition, SHM/DP is expected to deliv-er economic returns through data-informed lifecycle management decisions, surpassing the estimated costs encompassing the design, initial deployment operations, and maintenance of the SHM/DP system. Therefore, it is prudent to approach a system design problem with a cost-benefit analysis perspective, aiming to achieve a design that maximizes the Value of Information (VoI) ac-quired by an information-acquisition system, evaluated in a pre-posterior sense. In this paper, our focus is on a sensor optimization framework that aims to maximize the VoI over the lifecycle of a miter gate structure. We em-ploy the proposed Bayesian sensor optimization framework to assess the ef-fectiveness of various Value of Information (VoI) metrics in quantifying the overall benefits of an SHM/DP system. Additionally, we explore the impact of the decision maker's risk profile and the adopted decision policies on the system design.