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A sparse self-estimated sensor-network for reconstructing moving vehicle forces

机译:用于重建移动车辆力的稀疏自估计传感器网络

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摘要

Reconstructing moving vehicle forces from structural responses is an important inverse problem in bridge engineering. When a sensor-network is applied to moving force identification (MFI), a necessary task is to specify the contribution of different sensors at the first beginning. This paper proposes a novel method based on the sparse self-estimated sensor-network for estimating moving vehicle forces on bridges. Firstly, an over-completed dictionary is pre-defined to ensure a sparse representation of moving vehicle forces. Images corresponding to force atoms, i.e. structural responses caused by unit force components, are used to match the structural responses. As a self-estimated sensor-network, the signal features, noise energy and signal-to-noise ratio of each sensor can be self-estimated by combining sparse regularization and Bayesian information criterion. Then an improved L-2-norm regularization model, in which the cost function is defined by the weighted residuals of sensors, is proposed and applied to solve the MFI problem. Finally, both numerical and experimental examples are conducted to assess the accuracy and the feasibility of the proposed method. Illustrated results clearly show the robustness and applicability of the proposed method. Some related issues are discussed as well.
机译:从结构响应重建移动的车辆力是桥梁工程中的重要逆问题。当传感器网络应用于移动力识别(MFI)时,必要的任务是在第一个开始时指定不同传感器的贡献。本文提出了一种基于稀疏自估计传感器网络的新方法,用于估计桥梁上的移动车辆力。首先,预先完成的字典是预先定义的以确保移动车辆力的稀疏表示。对应于力原子的图像,即由单位力分量引起的结构响应,用于匹配结构响应。作为自估计传感器网络,通过组合稀疏正则化和贝叶斯信息标准,可以自估计每个传感器的信号特征,噪声能量和信噪比。然后,提出并应用了由传感器的加权残差来定义成本函数的改进的L-2-Norm正则化模型,以解决MFI问题。最后,进行了数值和实验实施例以评估所提出的方法的准确性和可行性。所示结果清楚地表明了所提出的方法的鲁棒性和适用性。还讨论了一些相关问题。

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