[1]焦美菊,郝建名,陈露丹,等.基于长期监测的车辆荷载效应时变极值预测[J].郑州大学学报(工学版),2023,44(06):105-111.[doi:10.13705/j.issn.1671-6833.2023.03.020]
　JIAO Meiju,HAO Jianming,CHEN Ludan,et al.Time-variant Extreme Value Prediction of Vehicle Load Effect Based on Long-term Monitoring[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):105-111.[doi:10.13705/j.issn.1671-6833.2023.03.020]
点击复制

基于长期监测的车辆荷载效应时变极值预测()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年06期
页码:
105-111
栏目:
出版日期:
2023-12-25

文章信息/Info

Title:
Time-variant Extreme Value Prediction of Vehicle Load Effect Based on Long-term Monitoring
作者:
焦美菊;  郝建名;  陈露丹;  郑元勋
郑州大学 水利与交通学院,河南 郑州 450001
Author(s):
JIAO Meiju;  HAO Jianming;  CHEN Ludan;  ZHENG Yuanxun
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
关键词:
极值理论;  监测信息;  区组超阈值模型;  车辆荷载;  模型检验
Keywords:
extreme value theory;  monitoring information;  block over-threshold model;  vehicle load;  model verification
分类号:
U441+. 2U448. 22
DOI:
10.13705/j.issn.1671-6833.2023.03.020
文献标志码:
A
摘要:
为建立准确合理的车辆荷载效应极值的概率模型,基于现有的车辆荷载效应极值理论和极值模型,提出 一种新的预测模型———区组超阈值模型。 首先,采用阈值模型,充分利用已有的监测信息建立相应的超阈值模型; 其次,研究超阈值模型和区组最大值模型之间的参数关联,通过点过程构建 2 种模型之间转换的桥梁,由已建立的 阈值模型推导区组最大值模型,进而得到任意使用年限内车辆荷载效应的最大值模型;最后,检验所建立的区组超 阈值模型,并结合桥梁的设计使用年限,利用模型外推了 10、40、70、100 a 车辆荷载应变的概率密度。 对某斜拉桥 跨中截面 5 个钢纵向传感器所采集的数据进行建模分析,结果表明:模型诊断图中的 P-P 图和 Q-Q 图都具有非常 良好的线性,经验重现水平所描绘的点均在重现水平的置信度为 95%的置信区间内,经验概率密度直方图也与相 应的 GP 分布完美拟合,均证明模型能够很好地模拟和预测实际车流作用下的车辆荷载效应极值。
Abstract:
In order to establish an accurate and reasonable probability model for extreme value of vehicle load (VL) effect, based on the existing extreme value theories and models of VL effect, a novel prediction model named block over-threshold model was proposed. Firstly, the over-threshold model was established by making full use of the existing monitoring information according to POT method; and then the parameter correlation between the overthreshold model and the block maximum model was studied. A bridge was constructed for their conversion through a point process,afterward the block maximum model was derived from established GPD model. Thus the maximum distribution of VL effect in any T period was predicted. Finally, the established block over-threshold model was tested, and the probability density of VL strain in 10, 40, 70 and 100 a was extrapolated by using the model in combination with bridge design service life. The data collected by five steel longitudinal sensors in mid-span section of a cable-stayed bridge were analyzed and modeled. The results showed that the P-P diagram and Q-Q diagram in the model diagnostic plots all had very good linearity, the points described by the empirical recurrence level were within the 95% confidence interval of the recurrence level, and the empirical probability density histogram also fitted perfectly with the corresponding GP distribution. They all proved that the model could well simulate and predict the extreme value of VL effect with actual traffic flow.

参考文献/References:

[1] 鲁乃唯, 刘扬, MOHAMMAD N. 交通量持续增长下大 跨桥梁时变极值外推分析 [ J] . 工程力学, 2018, 35 (7) : 159-166. 

LU N W, LIU Y, MOHAMMAD N. Extrapolation of time-variant extreme effect on long-span bridge considering steadily growing traffic volume[ J] . Engineering Mechanics, 2018, 35(7) : 159-166. 
[2] 中华人民共和国交通运输部. 公路桥涵设计通用规 范: JTG D60—2015 [ S ] . 北 京: 人 民 交 通 出 版 社, 2015. 
Ministry of Transport of the People′s Republic of China General specifications for design of highway bridges and culverts: JTG D60—2015[ S] . Beijing: China Communications Press, 2015.
 [3] GU Y M, LI S L, LI H, et al. A novel Bayesian extreme value distribution model of vehicle loads incorporating decorrelated tail fitting: theory and application to the Nanjing 3rd Yangtze River Bridge [ J ] . Engineering Structures, 2014, 59: 386-392.
 [4] OBRIEN E J, ENRIGHT B, GETACHEW A. Importance of the tail in truck weight modeling for bridge assessment [ J] . Journal of Bridge Engineering, 2010, 15(2) : 210- 213.
 [5] XIA M, CAI C S, PAN F, et al. Estimation of extreme structural response distributions for mean recurrence intervals based on short-term monitoring [ J ] . Engineering Structures, 2016, 126: 121-132. 
[6] OBRIEN E J, BORDALLO-RUIZ A, ENRIGHT B. Lifetime maximum load effects on short-span bridges subject to growing traffic volumes [ J] . Structural Safety, 2014, 50: 113-122.
 [7] ZHOU X Y, SCHMIDT F, TOUTLEMONDE F, et al. A mixture peaks over threshold approach for predicting extreme bridge traffic load effects [ J] . Probabilistic Engineering Mechanics, 2016, 43: 121-131.
 [8] 周军勇, 石雪飞, 阮欣. 多事件混合影响的桥梁车辆 荷载效应组合极值预测[ J] . 哈尔滨工业大学学报, 2018, 50(9) : 11-18.
 ZHOU J Y, SHI X F, RUAN X. Composite extrema prediction of multi-event driven bridge traffic load effects [ J] . Journal of Harbin Institute of Technology, 2018, 50 (9) : 11-18. 
[9] LEAHY C, OBRIEN E, O′ CONNOR A. The effect of traffic growth on characteristic bridge load effects [ J ] . Transportation Research Procedia, 2016, 14: 3990 -3999. 
[10] OBRIEN E J, SCHMIDT F, HAJIALIZADEH D, et al. A review of probabilistic methods of assessment of load effects in bridges [ J] . Structural Safety, 2015, 53: 44 -56.
 [11] COLES S. An introduction to statistical modeling of extreme values[M] . London: Springer Press, 2001. 
[12] HU P, ZHOU X, ZHOU Z Y. On the modelling of maximum field distribution within reverberation chamber using the generalized extreme value theory [ C ] ∥2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO) . Piscataway:IEEE, 2021: 1-4.
 [13] XIA H W, NI Y Q, WONG K Y, et al. Reliability-based condition assessment of in-service bridges using mixture distribution models[ J] . Computers & Structures, 2012, 106 / 107: 204-213.
 [14] LIU M, FRANGOPOL D M, KIM S. Bridge safety evaluation based on monitored live load effects[ J] . Journal of Bridge Engineering, 2009, 14(4) : 257-269. 
[15] VAN DE VYVER H. A multiscaling-based intensity-duration-frequency model for extreme precipitation [ J ] . Hydrological Processes, 2018, 32(11) : 1635-1647. 
[16] SHAO Y H, WU J M, LI M. Study on quantile estimates of extreme precipitation and their spatiotemporal consistency adjustment over the Huaihe River Basin[ J] . Theoretical and Applied Climatology, 2017, 127 ( 1 ) : 495 -511. 
[17] LIU Y C, HUO X L, LIU Y, et al. Analyzing streamflow extremes in the upper Ürümqi River with the generalized Pareto distribution [ J ] . Environmental Earth Sciences, 2015, 74(6) : 4885-4895.
 [18] HOSSAIN I, IMTEAZ M, KHASTAGIR A. Study of various techniques for estimating the generalised extreme value distribution parameters[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1067(1): 012065.
 [19] 翟科玮, 杜朝伟, 郑凤玺, 等. 在役钢筋混凝土 T 形 梁桥承载能力的模糊随机评估法[ J] . 郑州大学学报 (工学版) , 2017, 38(4) : 88-93. 
ZHAI K W, DU C W, ZHENG F X, et al. Load bearing capacity of existing highway RC T beam bridge based on fuzzy stochastic comprehensive evaluation[ J] . Journal of Zhengzhou University ( Engineering Science) , 2017, 38 (4) : 88-93.
 [20] 梁岩, 张卓航, 班亚云, 等. 多跨连续梁-刚构桥地震 易损性分析[ J] . 郑州大学学报( 工学版) , 2023, 44 (1) : 96-102. 
LIANG Y, ZHANG Z H, BAN Y Y, et al. Seismic fragility analysis of multi-span continuous girder-rigid frame bridges[ J] . Journal of Zhengzhou University ( Engineering Science) , 2023, 44(1) : 96-102.

更新日期/Last Update: 2023-10-22