Abstract

Bridge bearings experience numerous small-amplitude displacements under environmental loads. The continuous cyclic accumulations of these small-amplitude displacements will result in severe wear on the poly-tetra-fluoro-ethylene (PTFE) plates in the bridge bearings, which seriously endangers the service life of bearings. Traditional method directly uses the linear wear rate of cumulative displacements in a short period to evaluate the wearing life, but the linear wear rate only in a short period such as several days may not represent the characteristics in the whole bridge service life. Hence, this research takes the spherical steel bearings of the Nanjing Dashengguan Yangtze River Bridge as a study object. The cumulative dynamic displacement (CDD) under the action of a single train and the cumulative bearing travel (CBT) under the continual actions of many trains are studied using the monitored longitudinal displacement data from spherical steel bearings. Furthermore, the probability statistics and the Monte Carlo sampling simulation for CDD are studied, and the safety evaluation method for bearing wear life in the real environment is proposed using a reliability index regarding the failure probability of monitored CBT over the wear limit during service lifetime. In addition, safety evaluation on the bearing wear life was performed to assess the condition of spherical steel bearings in the real service environment. The results can provide an important reference for analysis on the bearing wear life of long-span railway bridge structures.

Highlights

  • As the service time of railway bridges increases, some bridge components may suffer from the aging and damage problems due to the influence of external environment loads

  • The cumulative probabilities of Ni,u and Ni,d are calculated based on probability statistics theory, and the cumulative distribution function is fitted in a least-squares manner using the cumulative probabilities of Ni,u and Ni,d, which is illustrated in two steps as follows: (1) e cumulative probabilities of Ni,u and Ni,d are calculated as follows: P􏼐Ni,u(j)􏼑

  • The probability statistics and the Monte Carlo sampling simulation method for cumulative dynamic displacement (CDD) are studied, and the safety evaluation method for the bearing wear life is proposed using a reliability index regarding the failure probability of monitored cumulative bearing travel (CBT) over the wear limit in the service lifetime. e main conclusions are drawn as follows: (1) rough monitored data analysis, the monitored longitudinal displacement data mainly consist of temperature-induced static displacements and traininduced dynamic displacements. e wavelet packet decomposition method can effectively extract the small-amplitude dynamic displacements caused by train loads from the monitored data

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Summary

Research Article

Received 22 June 2018; Revised 30 September 2018; Accepted 14 October 2018; Published 1 November 2018. E continuous cyclic accumulations of these small-amplitude displacements will result in severe wear on the poly-tetra-fluoro-ethylene (PTFE) plates in the bridge bearings, which seriously endangers the service life of bearings. E cumulative dynamic displacement (CDD) under the action of a single train and the cumulative bearing travel (CBT) under the continual actions of many trains are studied using the monitored longitudinal displacement data from spherical steel bearings. The probability statistics and the Monte Carlo sampling simulation for CDD are studied, and the safety evaluation method for bearing wear life in the real environment is proposed using a reliability index regarding the failure probability of monitored CBT over the wear limit during service lifetime. Safety evaluation on the bearing wear life was performed to assess the condition of spherical steel bearings in the real service environment. E results can provide an important reference for analysis on the bearing wear life of long-span railway bridge structures

Introduction
Bottom bearing plate
Dynamic displacement
Calculation equation
Passed train
Cumulative probability
Monitored curve WD
Monitored result Simulated result
Cumulative probability GEVD
Conclusions
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