基于贝叶斯原理的既有桥梁车辆荷载效应概率分布模型研究

发布时间：2018-01-03 14:08

本文关键词：基于贝叶斯原理的既有桥梁车辆荷载效应概率分布模型研究　出处：《华南理工大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着我国交通事业的发展，机动车辆的数量不断增长，公路桥梁的数量也不断增加，桥梁构件承载能力评估的重要性日益凸显。车辆荷载效应作为公路桥梁的主要可变荷载效应，直接反映了桥梁所承受的车辆荷载情况，因此桥梁车辆荷载效应的确定对于桥梁构件承载能力评估具有重要意义。公路桥梁的车辆荷载效应可以采用概率模型进行描述。我国现行公路桥梁设计规范中的车辆荷载效应概率分布模型是依据上世纪90年代全国4个具有代表性的WIM(Weigh In Motion)测点的实测自然车队确定的，计算采用的桥型结构主要为简支梁和多跨连续梁。在考虑不同时间、不同地域以及不同桥梁结构形式的前提下，规范的车辆荷载效应模型已不能满足当前既有桥梁构件承载能力评估的需求，因此本文针对既有桥梁的车辆荷载效应概率分布模型进行研究。既有桥梁车辆荷载效应概率分布模型的研究包括车辆荷载效应截口分布及目标使用期内车辆荷载效应最大值分布的研究。其中车辆荷载效应截口分布反映的是某一时间截口处既有桥梁的受载情况，随着桥梁运行时间的推移，车辆荷载效应截口分布需要不断更新；而目标使用期内车辆荷载效应最大值分布则反映了目标使用期内既有桥梁的受载情况。本文的主要工作内容包括： (1)车辆荷载效应样本的获取。采用新光大桥桥址附近区域具有代表性的WIM系统实测自然车队对新光大桥进行加载，利用车辆荷载效应影响面得到新光大桥构件车辆荷载效应样本。 (2)车辆荷载效应截口分布的确定。采用极大似然估计法和皮尔逊2检验法对新光大桥车辆荷载效应概率分布模型进行参数估计和拟合检验，确定新光大桥车辆荷载效应截口分布的分布类型及参数。 (3)车辆荷载效应截口分布的更新。确定了包含更新方法、更新路线、更新周期等方面在内的车辆荷载效应截口分布更新策略，并提出一种基于贝叶斯原理的车辆荷载效应截口分布更新方法，利用少量效应样本对车辆荷载效应截口分布进行更新。 (4)目标使用期内车辆荷载效应最大值分布的确定。引入目标使用期的概念，并基于平稳二项随机过程假设，由车辆荷载效应截口分布推导目标使用期内的车辆荷载效应最大值分布，确定可直接用于构件承载力评估的对应于0.95分位数的车辆荷载效应标准值。本文的主要结论包括： (1)基于新光大桥桥址附近区域的实测自然车队，计算分析得到的一般运行状态下和密集运行状态下的新光大桥车辆荷载效应截口分布都服从对数正态分布。 (2)所提出的新光大桥车辆荷载效应截口分布的更新策略，包括更新方法、更新路线、更新周期及更新中初始参数的选取，能够满足新光大桥健康监测系统全桥构件承载能力评估的需求，并可为其他既有桥梁的车辆荷载效应截口分布的更新提供思路。 (3)基于贝叶斯原理的车辆荷载效应截口分布更新方法，可针对车辆荷载效应截口分布对数正态模型，采用少量较新的车辆荷载效应试验样本，实现对多样本的车辆荷载效应先验截口分布的小幅修正。 (4)由已知的车辆荷载效应截口分布外推得到目标使用期内车辆荷载效应最大值分布及对应的车辆荷载效应标准值。结果显示，，由于一般运行状态下的车辆荷载分布比密集状态下离散程度大，因此一般运行运行状态下的车辆荷载效应最大值分布的标准差比密集运行状态下大，导致部分构件在一般运行状态下的车辆荷载效应标准值略大于密集运行状态下的标准值。
[Abstract]:With the development of transportation, the number of motor vehicles is growing, the number of highway bridges is increasing, the importance of evaluating the bearing capacity of bridge components has become increasingly prominent. The main effect of vehicle load effect as highway bridge variable load directly reflects the situation of the bridge under the vehicle load, so the vehicle load effect of bridge for sure has an important significance to evaluate the bearing capacity of the bridge structure.
Vehicle load effect of highway bridges can be described by probabilistic model. Our current code for design of highway bridges in the vehicle load effect probability distribution model is based on the last century, the 90s national 4 representative WIM (Weigh In Motion) measured natural team points are identified, the calculation of bridge structure is the main beam and multi span continuous beam. In consideration of different time, different regions and different forms of bridge structure under the premise, vehicle load effect model specification can not meet the current needs of both the assessment of carrying capacity of bridge components, so the article studied the effect of vehicle load of the existing bridge probability distribution model.
Both of the probability distribution model of load effect of bridges and vehicles including section distribution and target vehicle load effect using the vehicle load effect period distribution of the maximum value. The effect of vehicle load distribution section is reflected in a section at the time of the existing bridge load, with the bridge running time, vehicle load effect section the distribution of the need to constantly update; and the target using the vehicle load effect during the period of maximum distribution reflects the target use period of the existing bridge load.
The main contents of this paper are as follows:
(1) acquisition of vehicle load effect samples. A representative WIM system is used to load the Xinguang Bridge in the vicinity of the Xinguang Bridge site. The vehicle load effect sample of Xinguang Bridge component is obtained by vehicle load effect surface.
(2) determine the section distribution of load effect of vehicles. By using the maximum likelihood estimation method and Pearson parameter estimation and fitting test of Xinguang Bridge vehicle load effect probabilistic distribution model of 2 test, to determine the effect of vehicle load of Xinguang Bridge section distribution type and parameters.
(3) update the section distribution of traffic load effect. The included update method, update the route update period, the effect of vehicle load distribution section update strategy, and put forward a kind of vehicle load effect Bayesian updating method based on the principle of section distribution, with a small amount of sample and effect the effect of vehicle load distribution section to be updated.
(4) the target using the vehicle load effect during the period of maximum distribution is determined. By introducing the concept of the target period, and based on the stationary random process by the two hypothesis, the effect of vehicle load distribution is derived using the section target vehicle load effect within the period of maximum distribution, determine the standard vehicle load effect can be directly used for the corresponding evaluation of bearing capacity of member in the 0.95 percentile value.
The main conclusions of this paper include:
(1) the measured natural Fleet area near the Xinguang Bridge Site Based on the calculation and analysis of Xinguang Bridge effect of vehicle load distribution section obtained under normal operating state and intensive operation conditions are subject to lognormal distribution.
(2) the new bridge vehicle load effect section distribution update strategy, including update method, update the route, select the initial parameter update cycle and update, to meet the full bridge bearing capacity evaluation of Xinguang Bridge Health monitoring system needs, and can provide reference for other bridges with vehicle load effect section the distribution of the update.
(3) the effect of vehicle load distribution Bayesian updating method based on the principle of the section, the vehicle load effect section lognormal distribution model, a few samples of new vehicle load effect, to realize the multi sample vehicle load effect prior distribution of small amplitude correction section.
(4) from the known effect of vehicle load distribution section extrapolated target using the vehicle load effect within the period of maximum standard vehicle load effect distribution and corresponding values. The results show that the vehicle loads are generally running under the condition of intensive distribution than the discrete degree, because of the effect of vehicle load the general operation condition of maximum the value of standard deviation of the distribution of intensive operation state, the standard vehicle load effect leads to some components in the normal operating state was slightly higher than the standard value of intensive operation.

【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U441.2

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