Bridges play an essential role in the current transportation system that connects places separated by physical obstacles, natural or artificial. Guaranteeing the safety of the traveling public is the most important target for owners to maintain bridges. A bridge refers to a complex structural system composed of various structural and non-structural components. Each component may have direct or non-direct interactions with other components simultaneously. Once a component loses its function, a simple elemental failure may lead to a chain reaction of compounded damage to components and may even lead to a structural system failure. This is the motivation for this research to employ Bayesian network method to estimate the failure risk of bridge by integrating components of a bridge. With such a structural failure risk assessment tool, bridge owners may conduct more effective bridge maintenance to increase the resilience of bridge structures and prevent those structures from suffering unrecoverable damage. This research proposes a Bayesian network method to identify the interactions among bridge components and integrate component failure probabilities due to common deterioration or environmental impact. While bridge systems may have been designed for certain notable disastrous events such as earthquakes or floods, these systems may have ignored other important structural factors such as the aging of material, corrosion, and overloaded traffic flow, all of which reduce component functionality gradually over time. This research provides a dependency on structural components based on general types of bridges and categorized them as super-structure and sub-structure. The proposed structural component dependency of the bridge system would fit in the Bayesian network to estimate the likelihood leading to partial, integral, or sudden failures of a bridge. To assess the effectiveness of this risk assessment tool, this study analyzes three hands-on cases, including a rehabilitation project of a link slab, girder deformation and movement with bearing rotation monitoring, and a simulated sub-structure and foundation system. One of the benefits of using a failure risk probability for bridge systems is that more accurate inspections or repairs for specific or multiple components could be more effectively determined and remedied. In the bridge maintenance stage, the more economical and efficient rehabilitation of critical components would be recommended based on the integral failure risk. The inspection result and the maintenance effort would be updated in the bridge’s failure probability network for bridge service life assessment. The outcome of this research would provide probabilities of potential structural damage and alerts of decreasing bridge performance. The objective indicators of structural failure risk are beneficial for bridge owners to conduct more effective bridge maintenance that may increase the resilience of bridge structures.