摘要
为了得到桥梁全寿命评估的温度数据,提出了一种基于实测数据的钢箱梁桥随机温度场模拟方法.首先,采用最小二乘估计和假设检验,对润扬大桥悬索桥扁平钢箱梁为期5年的实测温度和温差进行了统计分析;然后,提出了一种复杂统计模式下样本序列模拟的数值逆变换抽样方法,并建立了扁平钢箱梁的全寿命评估随机温度场.研究结果表明:扁平钢箱梁实测温度服从2个正态分布的加权和,实测温差则服从1个Weibull分布和1个正态分布的加权和;利用数值逆变换抽样方法模拟得到的样本的概率密度函数与目标概率密度函数吻合良好;模拟温度场能准确描述扁平钢箱梁的温度特性.因此,该数值逆变换抽样方法能够准确模拟大跨悬索桥扁平钢箱梁全寿命期的随机温度场.
A stochastic temperature field simulation method for steel box-girder bridge based on monitoring data is developed to provide sufficient temperature data for life-cycle evaluation. First, the least square estimation and hypothesis testing are employed to analyze the statistical data of monitoring temperature and thermal difference of the flat steel-box girder of the Runyang suspension bridge (RSB) in five years. Then, a numerical inverse transform sampling method is proposed to simulate sample series with the complex statistical model, and the life-cycle stochastic temperature field of the flat steel-box girder is simulated. The results show that the monitoring temperature of the flat steel-box girder can be described by the weighted sum of two normal distributions, while the thermal difference among test points can be presented by the weighted sum of the Weibull distribution and the normal distribution. The probability distribution function of the sample simulated by the numerical inverse transform sampiing method agrees well with the target probability distribution function. The simulated temperature field can accurately describe the temperature field's characteristics of the flat steel box-girder. There-fore, the numerical inverse transform sampling method can accurately simulate the life-cycle stochastic temperature field of the flat steel box girder of the long-span suspension bridge.
出处
《东南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2013年第5期912-916,共5页
Journal of Southeast University:Natural Science Edition
基金
国家自然科学基金资助项目(50978056
51278104)
河海大学中央高校基本业务费专项基金资助项目(2013B01414)
关键词
结构健康监测
全寿命评估
扁平钢箱梁
悬索桥
数值逆变换抽样方法
structural health monitoring
life-cycle evaluation
flat steel box-girder
suspensionbridge
numerical inverse transform sampling method