Traffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh-in-motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics-constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics-based loss function. The generative models are then learned by training a physics-constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles. The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively.

中文翻译：

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基于物理约束的生成对抗网络的概率车辆重量估计

交通信息在民用交通基础设施的设计和管理中起着重要作用。桥梁动态称重（BWIM）通过估算车辆参数（包括通过桥梁响应的重量）来提供有效的交通信息收集工具。大多数现有的BWIM算法很少根据车辆估计轴重（AW）的概率分布来考虑车辆重量的认知不确定性。本文提出了一种新的方法，用于使用物理约束的生成对抗网络（GAN）进行概率车辆重量估计。引入生成模型来描述估计的AW和桥响应的概率分布。通过最小化基于物理的损失函数来制定和实施对生成模型的物理约束。然后通过使用观察到的桥响应训练受物理约束的GAN来学习生成模型。进行了数值研究和现场测试，以证明该方法使用了具有代表性的公路桥梁和车辆。结果表明，该方法能够成功地捕获车辆重量估计中的不确定性，并提供考虑到的不同车辆类型和负载条件的估计AW的概率分布，从而可以增强BWIM在交通数据收集等相关任务中的应用。和卡车超载执法。根据数值研究和现场测试的结果，本案例中的AW和车辆总重的最大变异系数分别为0.55和0.11。