The load rating of a steel truss bridge is experimentally identified in this study using an improved Bayesian model updating algorithm. The initial element model is sequentially updated to match the static and dynamic characteristics of the bridge. For this purpose, a modified version of the Hamiltonian Monte Carlo (HMC) simulation is adopted for closed-form candidate generation that helps in faster convergence compared to the Markov Chain Monte Carlo simulation. The updated model works as a digital twin of the original structure to predict its load-carrying capacity and performance under proof or design load. The proposed approach incorporates in-situ conditions in its formulation and helps to reduce the risk involved in bridge load testing at its full capacity. The rating factor for each member is estimated from the updated model, which also indicates the weak links and possible failure mechanism. The efficiency of the improved HMC-based algorithm is demonstrated using limited sensor data, which can be easily adopted for other existing bridges.

本研究使用改进的贝叶斯模型更新算法通过实验确定钢桁架桥的额定载荷。初始单元模型依次更新以匹配桥梁的静态和动态特性。为此，哈密顿蒙特卡罗 (HMC) 模拟的修改版本被用于封闭形式的候选生成，与马尔可夫链蒙特卡罗模拟相比，它有助于更​​快地收敛。更新后的模型作为原始结构的数字孪生模型来预测其在证明或设计载荷下的承载能力和性能。提议的方法在其公式中结合了原位条件，并有助于降低在全负荷下进行桥梁荷载测试所涉及的风险。每个成员的评级因子是从更新的模型中估计出来的，这也表明了薄弱环节和可能的故障机制。使用有限的传感器数据证明了改进的基于 HMC 的算法的效率，这些数据可以很容易地用于其他现有桥梁。