Despite growing computational and sensing power, naval structural design and analysis remains focused minimizing structural system weight while ensuring that estimated stress levels remain below allowable thresholds. While this approach allows the rapid design of new vessels that meet existing structural requirements, it struggles to fulfill the U.S. Navy’s growing need for lifecycle support of aging assets. To address these new tasks, extensions of current structural analysis and design tools are required. This paper presents a comprehensive framework for managing fatigue cracks on aging assets by extending traditional design-stage approaches with Bayesian network-based model updating. A methodology of updating structural load estimates that is able to account for changing operational profiles is presented, allowing future fatigue loading to be predicted with increased confidence. A Dynamic Bayesian Network (DBN) approach is taken to represent the time-varying growth of fatigue cracks, including both shipboard inspection data and the updated loading. A robust reliability formulation is used to predict future crack growth risks based on the DBN formulation and the inspection data-to-date. An example is presented for a hypothetical sealift vessel. Finally, a discussion of the implications of such models on Navy design practice, operations, and maintenance is presented.