UMAP-KDPI based on PS-InSAR: an unsupervised algorithmic framework for urban bridge system maintenance strategy

Urban bridge systems underpin sustainable mobility and metropolitan productivity, yet assets exposed to similar geology, climate, and traffic loads can exhibit correlated displacement responses at the group level. Detecting such correlations is essential for resilience-oriented asset management because it enables prioritization of inspection and coordinated maintenance planning across a city. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) provides dense, city-scale displacement time series, but converting high-dimensional monitoring data into actionable and interpretable bridge cohorts remains challenging. This paper develops uniform manifold approximation and projection with kernel density–based pattern identification (UMAP-KDPI), an unsupervised algorithmic framework that transforms PS-InSAR time series into bridge-group maintenance decision units via interpretable manifold learning and complex network analysis. Displacement histories are embedded using UMAP-KDPI to preserve temporal shape similarity and to extract four characteristic displacement patterns. Each bridge is then represented by a pattern-distribution signature, from which a weighted bridge-to-bridge similarity network is constructed and partitioned using the Louvain community detection algorithm to identify cohesive deformation subnetworks. Using 35 real satellite acquisitions on eight major cross-river bridges in a major metropolis in Central China, UMAP-KDPI reveals two stable subnetworks with distinct displacement-pattern compositions. Spatial organization, displacement-field consistency, and sensitivity analyses support the validity and robustness of the identified subnetworks, providing an interpretable system-level basis for group-wise inspection, coordinated interventions, and early risk mitigation.