Bridges are a vital and significant component of Taiwans transportation infrastructure. Therefore, regular and comprehensive inspections of existing bridges are necessary to prevent damage and traffic disruption and reduce earthquake-related damage and casualties. However, due to the large number of bridges in Taiwan, the time and budget required to perform traditional structural analyses (preliminary assessment, detailed analysis) on every bridge to calculate yield acceleration (Ay) and collapse acceleration (Ac) values make doing so impractical. This paper integrates material degradation, pushover analysis, and artificial intelligence to create a new inference model as an alternative to traditional structural analysis. Historical cases are used to infer Ay and Ac values by mapping relationships between the preliminary assessment factors (input) of historical cases and detailed assessments of Ay and Ac values (output). Using the proposed inference model to predict Ay and Ac values, bridge maintenance planners can quickly and more cost effectively assess bridge earthquake damage probabilities as a guide to identifying priority bridge maintenance projects.