Study on the Support Displacement Variation Pattern and Intelligent Early-Warning Methods for Kilometer-Level Railway Bridges

Bridge support displacement is a crucial indicator for evaluating the deformation states of supports and main girders. In this study, innovative methods were established based on long-term monitoring data from two kilometer-scale railway bridges, aimed at early warning of main girder deformation consistency and assessment of support wear conditions. First, outliers were identified and eliminated using a moving interval generalized Grubbs method. Second, the variation patterns of support displacement induced by temperature and train loads were systematically analyzed. Third, an early-warning method was proposed based on the optimal probability distribution model of support displacement difference to determine the warning threshold for main girder deformation consistency. Additionally, a method for evaluating support activity performance using jamming parameters was introduced to quantitatively assess support wear conditions. This research demonstrates that the proposed methods provide novel and effective approaches for the early warning and assessment of support and girder deformation, contributing to enhanced structural health monitoring and maintenance strategies.