Abstract
Bridges are critical components of transportation infrastructure, yet many face structural and safety risks due to aging design and increasing traffic volumes. In Kansas, recent incidents involving vehicular collisions have highlighted the need for predictive frameworks that can proactively identify vulnerable bridges. This study introduces a multi-model framework for assessing bridge vulnerability under vehicular collision exposure by integrating crash, traffic, and structural datasets. An Occurrence Factor was calculated from crash frequency within spatial buffers (25–100 m), representing external risk, while internal resilience was modeled using sufficiency rating, age, and posted speed limits. Three analytical approaches: Rule-Based Scoring, Machine Learning (Random Forest and XGBoost), and Fuzzy Logic, were developed to compute Resilience Factors. A Hybrid model synthesized these outputs to derive a robust Heat Factor quantifying bridge-level vulnerability. Applied to 5638 bridges across Kansas, the framework produced consistent high-risk rankings and heatmap visualizations, revealing that structural resilience and collision exposure are largely independent. These findings support data-informed prioritization for inspection, retrofitting, and safety intervention.
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