Analytical Model and Abnormality Detection of the Fluid Viscous Damper in Railway Suspension Bridges Considering Performance Change

Abstract

Fluid viscous dampers (FVDs) in long-span bridges are prone to performance change, in which the gap effect caused by oil leakage and the parameter alteration induced by viscous material denaturation are two primary sources of change. These variations may negatively affect the safety of both the bridge and the damper, thus underlining the significance of performance assessment and abnormality detection. This study develops a Gap-Maxwell (G-M) model to simulate the restoring force characteristics of the FVD considering performance alteration and subsequently suggests identification methods for gap and parameter change to capture the condition variation of the damper. The G-M model contains a gap–hook element group and a Maxwell element, where the gap length of the gap element represents the leakage, and the parameter change is achieved by setting different parameter values for the Maxwell element. Its feasibility is verified by comparison with the cyclic test results. The simplified longitudinal movement pattern for the railway suspension bridge during the operational stage is suggested. Based on the G-M model and the movement pattern, the segmental gap identification (SGI) method is proposed to determine the gap length by segmenting the original data and identifying the gap in each segment. Numerical simulations illustrate its accuracy and robustness under different damper parameter settings and noise pollution. The G-M model parameter identification (GMPI) procedure is raised to capture the parameter change, which follows a procedure of preprocessing, clustering, fitting, and optimization. It is numerically proved to be effective in identifying the damping coefficient and velocity exponent of the FVD.