###### Abstract
In contrast, the present study introduces a novel bridge maintenance prioritization framework that emphasizes both practical feasibility and nationwide scalability. By integrating nationally verified inspection indicators and adopting a dual-index framework comprising a Bridge Maintenance Priority (BMP) score and a locally sensitive Priority Index (PI) score, this study aims to bridge the gap between data-driven assessment and operational decision-making. This model prioritizes maintenance needs by considering a range of factors, including a bridge's risk index, criticality index, comparison index, and weighted factors from the PI calculation guide provided by the Korea Infrastructure Safety Corporation (KISC). It is designed to evaluate and prioritize bridge maintenance using relevant data [20]. The model considers 12 factors, including safety grade, service life, design load, seismic design status, annual average daily traffic volume (AADT), secondary damage in the event of bridge collapse, facility type, and the number of lanes. Additionally, the PI calculation guide assigns weights based on facility types and superstructure forms, taking into account damage types, component types, performance types, and grade types [21]. Factors related to the risk score were assessed using the Analytic HierarchyProcess (AHP) [22]. To ensure the reliability of the AHP survey, statistical software such as the Statistical Package for the Social Sciences (SPSS, version 29.0) was used to calculate [PERSON]'s alpha values. The weighted scores for different superstructure types were applied based on detailed safety inspection reports from the Korea Expressway Corporation. The bridge prioritization algorithm was then applied to and evaluated on nine bridges with detailed safety inspection reports. This study expands on the authors' previous work [23], which proposed a prioritization model using AHP-derived risk, criticality, and comparison scores. While the authors' previous study proposed a prioritization model based on risk, criticality, and comparison scores using the AHP, the present study advances the methodology by incorporating a PI that reflects component-level defect conditions derived from detailed safety inspection data. Unlike the previous approach, which relied primarily on overall safety ratings and generalized factor weights, the proposed model introduces a dual-index framework that combines both the BMP score and the PI score. This structure enables more refined and field-responsive prioritization by capturing localized structural vulnerabilities, thereby enhancing its applicability in real-world maintenance planning and emergency decision-making. Relying solely on the BMP system presents a critical limitation, as it does not fully reflect structural-specific risk factors. To overcome this, the PI was developed as a supplementary index that integrates parameters derived from national inspection data, aiming to simplify the evaluation procedure while enhancing the reliability and transparency of the prioritization process. Furthermore, the proposed model is designed to rapidly and systematically derive maintenance priorities for large bridge inventories, ultimately supporting the efficient allocation of limited budgets and facilitating timely investment decisions.