杨干, 陈适之, 钟强铭, 王俊峰, 蒋鑫, 刘时雨. 基于数据驱动加速的随机车流-桥梁耦合振动分析方法[J]. 工程力学, 2023, 40(8): 213-223. DOI: 10.6052/j.issn.1000-4750.2022.01.0065
引用本文: 杨干, 陈适之, 钟强铭, 王俊峰, 蒋鑫, 刘时雨. 基于数据驱动加速的随机车流-桥梁耦合振动分析方法[J]. 工程力学, 2023, 40(8): 213-223. DOI: 10.6052/j.issn.1000-4750.2022.01.0065
YANG Gan, CHEN Shi-zhi, ZHONG Qiang-ming, WANG Jun-feng, JIANG Xin, LIU Shi-yu. ANALYSIS APPROACH OF STOCHASTIC TRAFFIC-BRIDGE COUPLING VIBRATION BASED ON DATA-DRIVEN ACCELERATION[J]. Engineering Mechanics, 2023, 40(8): 213-223. DOI: 10.6052/j.issn.1000-4750.2022.01.0065
Citation: YANG Gan, CHEN Shi-zhi, ZHONG Qiang-ming, WANG Jun-feng, JIANG Xin, LIU Shi-yu. ANALYSIS APPROACH OF STOCHASTIC TRAFFIC-BRIDGE COUPLING VIBRATION BASED ON DATA-DRIVEN ACCELERATION[J]. Engineering Mechanics, 2023, 40(8): 213-223. DOI: 10.6052/j.issn.1000-4750.2022.01.0065

基于数据驱动加速的随机车流-桥梁耦合振动分析方法

ANALYSIS APPROACH OF STOCHASTIC TRAFFIC-BRIDGE COUPLING VIBRATION BASED ON DATA-DRIVEN ACCELERATION

  • 摘要: 面向随机车流的公路桥梁安全评估往往需要大量的车-桥耦合振动分析,然而,目前相关既有模拟方法运算效率有限,致使完成评估所需的分析耗时过久,无法及时提供评估结果。为此,该文以传统车-桥耦合振动分析理论为基础,提出了一种基于双向长短期记忆(Bi-directional Long Short-Term Memory, BiLSTM)算法加速的随机车流-桥梁耦合振动分析方法。该方法可以从初期桥梁响应时程快速映射出对应的后序响应,相较传统方法压缩了部分迭代求解周期,从而提高整体计算效率。随后,选取四座常见的桥梁作为分析对象,同时针对性地提出加权平均绝对百分比误差(WMAPE)、加权决定系数(WR2)等改进评价指标,并以传统迭代法为参照,分析了该方法的精度、鲁棒性以及计算效率。分析结果显示:该文提出的方法有较好的鲁棒性,在不同随机车流密度、不同路面粗糙度等工况下对桥梁的弯矩、剪力、挠度等响应都具有较高的分析精度;与传统方法相比,该方法可以在WMAPE小于3.2%、峰值绝对误差(PAE)小于2.9%以及WR2大于0.98的情况下,将随机车流下车-桥耦合振动响应的计算效率平均提高37.98%。这表明该方法可以在保证精度的前提下,有效提升随机车流-桥梁耦合振动分析效率,具备应用于桥梁结构快速分析、评估的潜力。

     

    Abstract: To evaluate the safety of highway bridges due to a random traffic flow, a large number of vehicle-bridge coupling simulations are demanded. However, the efficiency of current related simulation methods is generally limited, which causes the necessary analysis for the safety evaluation is time-consuming and cannot provide an assessment result in time. Therefore, based on the traditional analysis theory for vehicle-bridge coupling vibrations, this paper proposed an approach accelerated by Bi-directional Long Short-Term Memory (BiLSTM) algorithm. This method uses the initial period of bridge response to directly map out the subsequent bridge response, which avoids parts of the iterations needed in traditional calculation methods, leading to the improvement of the overall calculation efficiency. Then, four common bridges were selected as objects and some improved evaluation indicators such as the weighted mean absolute percentage error (WMAPE) and the weighted coefficient of determination (WR2) were proposed specifically. The accuracy, robustness, and calculation efficiency of the proposed method were analyzed in comparison with the traditional simulation method. The results show that the proposed method has a good robustness which attains the high accuracy for predicting the bending moment, the shearing force and, the deflection of bridges under different random traffic densities or under different pavement roughness conditions. Compared with the traditional method, this method can improve the calculation efficiency by 37.98% on average as its WMAPE less than 3.2%, peak absolute error (PAE) less than 2.9%, and WR2 larger than 0.98. This indicates that this method could greatly improve the calculation efficiency of the stochastic traffic-bridge coupling vibration with high accuracy and has the potential for the rapid analysis and evaluation of bridge structures.

     

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