动态称重数据驱动的桥梁疲劳车辆模型优化

Weigh-in-Motion Data-driven Optimization of Bridge Fatigue Vehicle Models

摘要:

为提升区域交通差异下桥梁疲劳损伤评估的准确性，提出一种基于动态称重数据（WIM）的疲劳车辆模型优化方法。针对现行规范中固定参数模型在区域适应性不足的问题，引入疲劳损伤比作为核心指标，以跨中截面为关键分析位置，建立以疲劳损伤比趋近于1为目标的优化函数，并通过遗传算法协同标定车辆轴数构型、轴重比和轴距等参数，构建3轴/4轴区域适应性模型。以上海市某高架桥半年WIM数据为案例的分析表明：规范模型Ⅲ的疲劳损伤比普遍高于2.0，显著高估了实际损伤；优化后的4轴模型将疲劳损伤比稳定在1.0~1.2区间内。由此证实所提方法可有效降低模型误差，为区域差异化交通荷载下的桥梁疲劳寿命评估提供精准理论支撑。

Abstract:

To enhance the accuracy of fatigue damage assessment for bridges under regional traffic differences, an optimization method for fatigue vehicle models based on weigh-in-motion (WIM) data is proposed. In response to the insufficient regional adaptability of the fixed parameter model in current standards, the fatigue damage ratio is introduced as the core indicator. With the mid-span section as the key analysis position, an optimization function is established with the goal of the fatigue damage ratio approaching 1. And through genetic algorithms, the parameters such as the number of vehicle axles configuration, axle load ratio and wheelbase are collaboratively calibrated to construct a 3-axle / 4-axle regional adaptability model. Taking the half-year WIM data of a certain elevated bridge in Shanghai as a case, the analysis shows that the fatigue damage ratio of the standard model Ⅲ is generally higher than 2.0, significantly overestimating the actual damage. After optimization, the four-axle model stabilizes the fatigue damage ratio within the range of 1.0 to 1.2. The conclusion confirms that this method can effectively reduce the model errors and provide the precise theoretical support for the fatigue life assessment of bridges under regional differentiated traffic loads.