| 引用本文: | 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.DOI:10.11918/j.issn.0367-6234.201707130 |
| ZHOU Junyong,SHI Xuefei,RUAN Xin.Composite extrema prediction of multi-event driven bridge traffic load effects[J].Journal of Harbin Institute of Technology,2018,50(9):11.DOI:10.11918/j.issn.0367-6234.201707130 |
|
| 摘要: |
| 为解决受多事件混合影响的桥梁车辆荷载效应不满足独立和同分布假定，导致预测极值不准确的问题，提出采用共同阈值，改进组合广义帕累托分布(CGPD)极值预测模型，以适应采用任意尾部逼近函数预测基于事件分类的超阈值样本极值.对CGPD模型的关键问题，如超阈值样本的时间独立性检验、阈值选取和GPD参数估计，分别提出基于自相关系数的采样间隔法、基于K-S优度检验的自动计算法和概率权重矩法的实用解决方法.通过具有理论解的数值算例验证改进CGPD模型及其实用解决方法的准确性，并将CGPD方法应用于中小跨径桥梁和大跨径桥梁车辆荷载效应的极值预测中.结果表明：所提实用解决方法具有很好的应用效果，计算精度高，这些方法是CGPD应用的关键.数值算例验证了CGPD能精确预测受多事件混合影响的样本极值，与理论解的误差在3%以内，而采用传统混合数据的单一广义帕累托分布(SGPD)进行外推会产生很大偏差.中小跨径桥梁车辆荷载效应可以基于参与加载的货车数量划分加载事件，大跨径桥梁车辆荷载效应则可以根据时均交通量和时均货车比率划分时段形成不同加载事件，应用CGPD方法均能简单而方便地获取任意重现期的桥梁车辆荷载效应极值，而传统SGPD方法会形成显著的估计偏差，高达13.7%.
|
| 关键词: 桥梁工程 组合极值预测 多事件影响 车辆荷载效应 超阈值 广义帕累托分布 |
| DOI：10.11918/j.issn.0367-6234.201707130 |
| 分类号:U441.2 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(7,8);同济大学交通运输工程高峰学科开放基金(2016J012302) |
|
| Composite extrema prediction of multi-event driven bridge traffic load effects |
|
ZHOU Junyong,SHI Xuefei,RUAN Xin
|
|
(School of Civil Engineering, Tongji University, Shanghai 200092, China)
|
| Abstract: |
| To address the issue that the traffic load effects on bridges are neither independent nor identically distributed due to the influence of multiple events, the predictive method of composite generalized Pareto distribution (CGPD) was improved using joint threshold, which is robust to predict the extrema of mixture peaks over threshold (POT) using any tail approximation function. The autocorrelation coefficient informed sampling interval method, K-S test based threshold selection, and probability weight moment method were proposed to resolve the time-independent test of POTs, threshold selection, and parameter estimation in CGPD, respectively. Theoretical solutions were conducted to verify the accuracy of the improved CGPD model and its critical techniques, and the CGPD method was implemented in the extrema prediction of realistic traffic load effects on short to long span bridges. Results indicated the proposed practical techniques showed good application effect and generated accurate results, which provide strong support for the implementation of CGPD. Numerical examples showed the CGPD could precisely predict extreme values of multi-event driven samples with relative error below 3%. In contrast, the conventional single generalized Pareto distribution (SGPD) exhibits a significant deviations compared with CGPD. The realistic traffic load effects on short and medium span bridges can be categorized based on the events of number of trucks involved, but that on long span bridges can be categorized based on events in rush hours or normal hours according to the ratio of hourly traffic volume to hourly truck. The CGPD method is convenient to predict extreme load effect in any return period, whereas the SGPD method would produce significant deviation in extrema with a maximal relative error of 13.7% compared to that of CGPD.
|
| Key words: bridge engineering composite extreme prediction multi-vent driven traffic load effect peaks over threshold (POT) generalized Pareto distribution (GPD) |
