车辆实测数据表明，车辆荷载存在明显递增趋势。本文利用我国安徽省某桥梁长期健康监测数据，定义年平均小时交通量（AAHT）来考虑交通量的周期性和季节性变化，并据此建立季节性差分自回归移动平均模型(SARIMA)模型对未来交通荷载进行预测。同时，基于实测卡车数据的关键参数统计结果，建立多种类型的卡车荷载模型，逐一加载到某T梁桥有限元模型上，研究卡车荷载的非平稳增长对结构疲劳损伤的影响。结果表明，基于AAHT的季节性差分自回归移动平均模型能够准确预测车辆荷载，且车辆的非平稳增长对桥梁疲劳损伤影响显著，考虑车辆荷载非平稳增长时桥梁的疲劳损伤度约为不考虑车辆非平稳增长时的1.7倍。

The recorded traffic data shows that traffic loads have been increasing. In this study, based on the long-term health monitoring data collected from Anhui Province, the annual average hourly traffic (AAHT) is defined to take into account the periodical and seasonal change of traffic volumes, furthermore, an autoregressive moving average model (SARIMA) is established to simulate truck loads in the future. At the same time, several truck load models are developed with the statistics of the key parameters of truck data, and then loaded one by one on the finite element model of a T-bridge, to calculate the fatigue damage induced by the non-stationary increases of truck traffic. The results show that the AAHT-based SARIMA model is accurate and efficient for predicting traffic loads, moreover, the non-stationary increase of traffic loads will significantly jeopardize bridge structures due to the fatigue damage caused. Specifically, the fatigue damage will be increased by about 50% when considering this kind of non-stationary increase.

国家自然科学基金资助项目(51708069)；重庆市基础研究与前沿探索项目(cstc2018jcyjA2535)