Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada.
Sensors (Basel). 2023 May 28;23(11):5154. doi: 10.3390/s23115154.
With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge's displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy.
随着智能基础设施的兴起和发展，人们对在桥梁上安装自动监测系统的需求很大，而桥梁是交通网络的关键组成部分。在这方面，与传统的在桥上安装固定传感器的系统相比，利用安装在过往车辆上的传感器收集的数据可以降低监测系统的成本。本文提出了一种利用安装在过往车辆上的加速度计传感器确定桥梁响应并识别模态特性的创新框架。在提出的方法中，首先利用车辆轴的加速度响应确定桥梁上一些虚拟固定节点的加速度和位移响应。基于线性和新颖的三次样条形状函数的逆问题求解方法分别提供了桥梁位移和加速度响应的初步估计。由于逆解方法仅能够在车辆轴附近高精度地确定节点的响应信号，因此提出了一种新的基于自回归和外生时间序列模型（ARX）的移动窗口信号预测方法来完成误差较大区域（无效区域）的响应。利用奇异值分解（SVD）对预测位移响应和频域分解（FDD）对预测加速度响应的结果进行集成的新方法来识别桥梁的模态形状和固有频率。为了评估所提出的框架，考虑了移动质量作用下单跨桥的各种数值但现实的模型；研究了不同水平的环境噪声、过往车辆的轴数以及其速度对方法准确性的影响。结果表明，所提出的方法可以高精度地识别桥梁的三个主要模态的特性。
