根据称重运动数据预测桥梁的最大活载荷效应

Patrick Lou, Chan Yang, Hani Nassif
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摘要

基于运动中称重(WIM)测量的卡车荷载谱被用于开发特定场地的活荷载模型,以预测桥梁的最大荷载效应。传统的荷载外推法被用于制定 AASHTO 荷载和阻力系数设计 (LRFD) 桥梁设计规范,但很少有研究利用实际数据对荷载外推法的准确性进行评估。现行的 AASHTO《桥梁评估手册》(MBE)利用前 5%的荷载效应来推断 5 年最大荷载效应,以进行荷载分级。然而,在卡车运量大的情况下,由于选择了上尾部,使用 AASHTO MBE 预测的 5 年最大荷载效应明显低于观测值。因此,上尾尺寸的选择需要进一步验证。本文提出了对传统活荷载外推法的修改建议。首先,通过模拟确定不同重现期更精确的最大荷载值,并利用 7 年的连续数据进行验证。然后,利用不同的上尾部尺寸获得传统活荷载外推值,并与模拟值进行比较。当误差最小时,就确定了最佳上尾尺寸。研究结果表明,与基于百分比的方法相比,使用特定数量的卡车作为上尾可获得更高的精确度。具体而言,建议的卡车数量范围为 3,000 至 5,000 辆,最佳数量为 3,600 辆。本文最后向 AASHTO MBE 提出了提高活载荷外推法准确性的建议。
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Prediction of Maximum Live-Load Effects for Bridges Based on Weigh-in-Motion Data
Truck load spectra based on weigh-in-motion (WIM) measurements have been utilized in developing site-specific live load models to predict the maximum load effects on bridges. Conventional load extrapolation has been utilized to develop the AASHTO load-and-resistance factor design (LRFD) Bridge Design Specifications, while few studies have evaluated the accuracy of the load extrapolation techniques with actual data. The current AASHTO Manual for Bridge Evaluation (MBE) utilizes the top 5% of the load effects to extrapolate the 5-year maximum load effects for load rating. However, in the cases of high truck volume, the predicted 5-year maximum load effects using AASHTO MBE are significantly lower than the observed value because of the selection of the upper tail. Therefore, the choice of the upper tail size needs further validation. This paper proposes a modification to the conventional live load extrapolation method. Firstly, more accurate maximum load values for different return periods are determined through simulation and validated using 7 years of continuous data. Then, the values from conventional live load extrapolations using different upper tail sizes are obtained and compared with the simulation values. The optimal upper tail size is determined when the minimum error is yielded. The findings suggest that using a specific number of trucks for the upper tail yields greater accuracy compared to a percentage-based approach. Specifically, the recommended range is between 3,000 to 5,000 trucks, with an optimal number of 3,600. This paper concludes with recommendations to the AASHTO MBE to enhance the accuracy of live load extrapolation.
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