Uncovering Symmetric and Asymmetric Deterioration Patterns in Maryland’s Steel Bridges Through Time-Series Clustering and Principal Component Analysis

This study analyzes long-term deterioration patterns in 1378 Maryland steel bridges using annual Bridge Health Index (BHI) records from 1995–2021. Missing observations were addressed through linear interpolation combined with forward/backward filling, after which feature-wise z-score standardization was applied to ensure comparability across annual trajectories. Euclidean K-means clustering (k-means++ initialization, 10 restarts) was implemented to identify deterioration archetypes, with K = 6 selected using the elbow method and the silhouette coefficient. Cluster-internal stability was evaluated using bridge-level Root Mean Squared Error (RMSE), and uncertainty in median deterioration profiles was quantified using 2000-iteration percentile-based bootstrap confidence intervals. To interpret structural and contextual drivers within each group, Principal Component Analysis (PCA) was performed on screened and standardized geometric, structural, and traffic-related attributes. Results revealed strong imbalance in cluster membership (757, 503, 35, 33, 44, and 6 bridges), reflecting substantial diversity in long-term BHI behavior. Cluster-median RMSE values ranged from 2.69 to 22.66, while wide confidence bands in smaller clusters highlighted elevated uncertainty due to limited sample size. PCA indicated that span length, deck width, truck percentage, and projected future ADT were the most influential differentiators of deteriorating clusters, while stable clusters were distinguished by consistently high BHI component values and limited geometric complexity. Missing rehabilitation records prevented definitive attribution of U-shaped or recovering trajectories to specific intervention events. Overall, this study establishes a scalable, statistically supported framework for deterioration-trajectory profiling and provides actionable insight for proactive inspection scheduling, rehabilitation prioritization, and long-term asset management planning for state-level bridge networks.