基于支座动位移监测的高铁桥梁支座磨损状态安全评估
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摘要
基于大胜关长江大桥支座的纵向动位移监测数据,利用动位移监测值的累加概率特性拟合广义极值分布函数,通过蒙特卡洛抽样模拟得到在设计使用寿命内的动位移累积行程,基于支座超过磨损上限的失效概率进行支座磨损状态安全评价。结果表明:各个球型钢支座的动位移累积行程与列车通过次数具有明显的线性相关特性,且下游侧距固定支座最近支座的动位移累积行程的线性增长速率最大,在3495次列车作用下其动位移累积平均值最大(7.020mm),动位移累积行程可达到22 810mm,更容易磨损破坏;利用广义极值分布函数能够较好地模拟实测累加概率特性,下游侧距固定支座最近支座对应广义极值分布函数的形状参数、比例参数和位置参数分别为0.305 8,0.816 3和2.029 4;通过蒙特卡洛抽样得到的模拟累积行程与实测累积行程基本一致;在5%显著性水平上支座失效概率的计算值为0,说明支座在设计使用寿命内未达到磨损上限,处于安全状态。
Based on the monitoring data of longitudinal dynamic displacement in the bearings of Dashengguan Yangtze River Bridge,safety evaluation on the wear condition of bearing was carried out under real service environment.Firstly,the generalized extreme distribution function was fitted by the cumulative probability characteristics of the monitoring values of dynamic displacement.Secondly,the cumulative travel of dynamic displacement in the designed service life was obtained through Monte Carlo sampling simulation.Finally,the safety evaluation on the wear condition of bearing was carried out based on the failure probability of bearing exceeding the upper limit of wear.Results show that the cumulative travel of dynamic displacement for each spherical steel bearing has obvious linear correlation with the number of trains passing through,and the linear growth rate for the cumulative travel of dynamic displacement of the nearest bearing from the downstream side to the fixed bearing is the largest.Under the action of 3495 trains,the cumulative average of dynamic displacement is the largest(7.020mm),the cumulative travel of dynamic displacement can reach 22 810 mm,which is more prone to wear and failure.The cumulative probability characteristics of monitoring data can be well fitted by generalized extreme distribution function.The parameters of shape,scale and location for the nearest bearing from the downstream side to the fixed bearing corresponding to the generalized extreme distribution function are 0.305 8,0.816 3and2.029 4,respectively.The cumulative travel obtained from Monte Carlo sampling is basically the same as the monitored one.The calculated value of failure probability of the bearing is 0at the significant level5%,it indicates that the bearing has not reached the upper limit of wear in the designed service life and is in a safe state.
Based on the monitoring data of longitudinal dynamic displacement in the bearings of Dashengguan Yangtze River Bridge,safety evaluation on the wear condition of bearing was carried out under real service environment.Firstly,the generalized extreme distribution function was fitted by the cumulative probability characteristics of the monitoring values of dynamic displacement.Secondly,the cumulative travel of dynamic displacement in the designed service life was obtained through Monte Carlo sampling simulation.Finally,the safety evaluation on the wear condition of bearing was carried out based on the failure probability of bearing exceeding the upper limit of wear.Results show that the cumulative travel of dynamic displacement for each spherical steel bearing has obvious linear correlation with the number of trains passing through,and the linear growth rate for the cumulative travel of dynamic displacement of the nearest bearing from the downstream side to the fixed bearing is the largest.Under the action of 3495 trains,the cumulative average of dynamic displacement is the largest(7.020mm),the cumulative travel of dynamic displacement can reach 22 810 mm,which is more prone to wear and failure.The cumulative probability characteristics of monitoring data can be well fitted by generalized extreme distribution function.The parameters of shape,scale and location for the nearest bearing from the downstream side to the fixed bearing corresponding to the generalized extreme distribution function are 0.305 8,0.816 3and2.029 4,respectively.The cumulative travel obtained from Monte Carlo sampling is basically the same as the monitored one.The calculated value of failure probability of the bearing is 0at the significant level5%,it indicates that the bearing has not reached the upper limit of wear in the designed service life and is in a safe state.
引文
[1] YAN Bin,LIU Shi,ZHOU Min,et al.Impact of Bearing Friction Resistance on Mechanical Characteristics of Track Bridge System[C]//IABSE Conference,Guangzhou 2016:Bridges and Structures Sustainability-Seeking Intelligent Solutions.Guangzhou:International Association for Bridge and Structural Engineering,2016:173-179.
[2] WANG Gaoxin,DING Youliang,SONG Yongsheng,et al.Detection and Location of the Degraded Bearings Based on Monitoring the Longitudinal Expansion Performance of the Main Girder of the Dashengguan Yangtze Bridge[J].Journal of Performance of Constructed Facilities,2016,30(4):04015074.
[3] WANG Gaoxin,DING Youliang.Research on Monitoring Temperature Difference from Cross Sections of Steel Truss Arch Girder of Dashengguan Yangtze Bridge[J].International Journal of Steel Structures,2015,15(3):647-660.
[4] GUO Tong,LIU Jie,HUANG Lingyu.Investigation and Control of Excessive Cumulative Girder Movements of Long-Span Steel Suspension Bridges[J].Engineering Structures,2016,125:217-226.
[5] GUO Tong,LIU Jie,PAN Shenjun.Displacement Monitoring of Expansion Joints of Long-Span Steel Bridges with Viscous Dampers[J].Journal of Bridge Engineering,2014,20(9):2597-2610.
[6] FIGLUS T,LISCAK S,WILK A,et al.Condition Monitoring of Engine Timing System by Using Wavelet Packet Decomposition of an Acoustic Signal[J].Journal of Mechanical Science and Technology,2014,28(5):1663-1671.
[7] WALD R,KHOSHGOFTAAR T M,SLOAN J C.Feature Selection for Optimization of Wavelet Packet Decomposition in Reliability Analysis of Systems[J].International Journal on Artificial Intelligence Tools,2013,22(5):1360011.
[8]杨建伟,蔡国强,姚德臣,等.基于小波包变换和BP网络的铁道车辆滚动轴承故障诊断方法[J].中国铁道科学,2010,31(6):68-73.(YANG Jianwei,CAI Guoqiang,YAO Dechen,et al.Fault Diagnosis Method for the Rolling Bearing of Railway Vehicle Based on Wavelet Packet Transform and BP Neural Network[J].China Railway Science,2010,31(6):68-73.in Chinese)
[9]姚昌荣,李亚东.小波包变换频率混淆的消除及其在桥梁健康监测中的应用[J].中国铁道科学,2008,29(5):58-64.(YAO Changrong,LI Yadong.Frequency Aliasing Improvement of Wavelet Packet Transform and Its Application in Bridge Health Monitoring[J].China Railway Science,2008,29(5):58-64.in Chinese)
[2] WANG Gaoxin,DING Youliang,SONG Yongsheng,et al.Detection and Location of the Degraded Bearings Based on Monitoring the Longitudinal Expansion Performance of the Main Girder of the Dashengguan Yangtze Bridge[J].Journal of Performance of Constructed Facilities,2016,30(4):04015074.
[3] WANG Gaoxin,DING Youliang.Research on Monitoring Temperature Difference from Cross Sections of Steel Truss Arch Girder of Dashengguan Yangtze Bridge[J].International Journal of Steel Structures,2015,15(3):647-660.
[4] GUO Tong,LIU Jie,HUANG Lingyu.Investigation and Control of Excessive Cumulative Girder Movements of Long-Span Steel Suspension Bridges[J].Engineering Structures,2016,125:217-226.
[5] GUO Tong,LIU Jie,PAN Shenjun.Displacement Monitoring of Expansion Joints of Long-Span Steel Bridges with Viscous Dampers[J].Journal of Bridge Engineering,2014,20(9):2597-2610.
[6] FIGLUS T,LISCAK S,WILK A,et al.Condition Monitoring of Engine Timing System by Using Wavelet Packet Decomposition of an Acoustic Signal[J].Journal of Mechanical Science and Technology,2014,28(5):1663-1671.
[7] WALD R,KHOSHGOFTAAR T M,SLOAN J C.Feature Selection for Optimization of Wavelet Packet Decomposition in Reliability Analysis of Systems[J].International Journal on Artificial Intelligence Tools,2013,22(5):1360011.
[8]杨建伟,蔡国强,姚德臣,等.基于小波包变换和BP网络的铁道车辆滚动轴承故障诊断方法[J].中国铁道科学,2010,31(6):68-73.(YANG Jianwei,CAI Guoqiang,YAO Dechen,et al.Fault Diagnosis Method for the Rolling Bearing of Railway Vehicle Based on Wavelet Packet Transform and BP Neural Network[J].China Railway Science,2010,31(6):68-73.in Chinese)
[9]姚昌荣,李亚东.小波包变换频率混淆的消除及其在桥梁健康监测中的应用[J].中国铁道科学,2008,29(5):58-64.(YAO Changrong,LI Yadong.Frequency Aliasing Improvement of Wavelet Packet Transform and Its Application in Bridge Health Monitoring[J].China Railway Science,2008,29(5):58-64.in Chinese)