Accurate identifying moving load is of great significance for bridge design, operation and maintenance. As the condition state of actual bridges is complex, moving load is subjected to time-varying uncertainty. Traditional probability theory uses stochastic process to describe uncertain moving load time history, which relies on plenty of samples. This paper proposes a bridge moving load identification method based on interval process with a small number of samples. Firstly, the establishment approach for non-probabilistic interval process model of moving load is presented based on ellipsoidal convex model. Then, the interval process identification of moving load problem is transformed into median time history identification which is achieved by deterministic identification method, and radius time history identification which is realized by matrix decomposition. Finally, the effectiveness of the proposed method is verified through numerical and experimental examples. The results manifested that compared with the Monte Carlo method, the required sample amount of the prosed method is significantly reduced, while compared with the stochastic process method, the results obtained by the proposed method are more accurate with the same sample amount. Besides, the influences of vehicle weight and velocity, road roughness, measurement noise, and measurement location on the identification results are systematically investigated.

中文翻译：

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基于区间过程的桥梁移动荷载识别不确定

准确识别移动载荷对桥梁设计、运行和维护具有重要意义。由于实际桥梁的状况状态很复杂，因此移动载荷会受到时变不确定性的影响。传统概率论使用随机过程来描述不确定的移动载荷时间历史，这依赖于大量样本。本文提出了一种基于少量样本区间过程的桥梁移动载荷识别方法。首先，基于椭球凸模型，提出了移动载荷非概率区间过程模型的建立方法;然后，将动荷载问题的区间过程识别转化为通过确定性识别方法实现的中位时程识别，以及通过矩阵分解实现的半径时程识别。最后，通过数值和实验算例验证了所提方法的有效性。结果表明，与蒙特卡洛方法相比，prosed 方法所需的样本量显著降低，而与随机过程方法相比，在相同样本量下，所提方法获得的结果更准确。此外，系统研究了车辆重量和速度、道路粗糙度、测量噪声和测量位置对识别结果的影响。