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桥梁的服役安全问题一直广为人们关注。过去由于缺少实地通行车辆数据,新桥设计和旧桥评估都采用规范中的荷载模型。动态称重系统(WIM)能够提供准确的车辆信息,但利用WIM数据进行设计基准期内最大荷载效应估计时,又面临着概率模型在高分位点上不准确的问题。为解决这个问题,该文提出基于GPD模型的尾部拟合方法,以实现对基准期内车辆荷载效应极值较为准确的估计。该方法首先依据车辆荷载过程的特点对GPD模型的表达形式进行改进,然后利用最小均方差准则(MSE)对GPD模型中的参数进行估计,并对截尾GPD模型进行合理修正,从而得到修正的GPD模型。利用该模型,该文对国内某大桥实测车流量数据进行了系统分析和车辆荷载效应极值估计,并与规范方法进行了比较。结果表明:该文提出的基于GPD模型的计算方法能够合理预测未来车辆荷载效应的极值,而规范方法在中短评估期内对车辆荷载效应的估计存在偏低的风险。
The security of the bridge service has been widely concerned about. In the past due to the lack of data on vehicles in the field, the new bridge design and the assessment of the old bridge are used to regulate the load model. The Dynamic Weighing System (WIM) provides accurate vehicle information. However, using WIM data to estimate the maximum load effect during the design base period faces the problem of inaccurate probability models at high scores. In order to solve this problem, this paper proposes a tail fitting method based on GPD model to achieve a more accurate estimation of the extreme value of vehicle load effect during the reference period. The method first improves the expression of GPD model according to the characteristics of vehicle load process, then uses the least mean square error criterion (MSE) to estimate the parameters in GPD model, and modifies the truncated GPD model to obtain the modified GPD model. Using this model, the paper analyzes the measured traffic flow data of a certain bridge in China systematically and estimates the extreme value of vehicle load effect, and compares it with the normative method. The results show that the proposed method based on GPD model can reasonably predict the extreme value of future vehicle load effect, while the normative method poses a low risk to the estimation of vehicle load effect in short and medium evaluation period.