Study on the Support Displacement Variation Pattern and Intelligent Early-Warning Methods for Kilometer-Level Railway Bridges
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Railway Engineering and Dynamics · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering, Southeast University, Nanjing 210096, China
China Railway Bridge & Tunnel Technologies Co., Ltd., 8 Panneng Road, Nanjing 210061, China
State Key Laboratory of Safety, Durability and Healthy Operation of Long Span Bridges, Southeast University, Nanjing 211189, China
School of Architecture and Civil Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China · China Railway Engineering Corporation Science and Technology Research and Development Project
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