Improved Data-Driven Stochastic Subspace Identification with Autocorrelation Matrix Modal Order Estimation for Bridge Modal Parameter Extraction Using GB-SAR Data

Xianglei Liu,Songxue Zhao,Runjie Wang,Ming Huang,Peipei Wang

doi:10.3390/buildings12020253

Abstract

With the advantage of non-contact measurement, ground-based synthetic aperture radar (GB-SAR) has been widely used to obtain the dynamic deflection of various bridges. Data-driven stochastic subspace recognition (Data-SSI), a popularized time-domain technique, is commonly used for modal parameter identification of bridges. To improve the computational efficiency and accuracy of the Data-SSI method for bridge modal parameter estimation using GB-SAR, this paper proposes an improved Data-SSI method. First, boxplot data filtering is applied to screen out the error points to generate a Hankel matrix. Second, the Hankel matrix compression method is presented to reduce the ill-conditioned vectors in the column vectors of the Hankel matrix to improve calculation efficiency. Finally, the exact modal order (EMO) modal estimation algorithm based on the autocorrelation matrix is adopted to reduce the generation of false modes and improve the calculation efficiency. The results of simulation and field experiments show that the natural frequency values for the improved Data-SSI method are 2.3208 and 2.3189 and the damping ratio coefficient values are 8.10 and 8.08, under windows 1 and 2, respectively. The operation times using the improved Data-SSI method are 2.02 s and 7.61 s under windows 1 and 2, respectively. This proves that the proposed improved Data-SSI method has higher accuracy and computational efficiency.

Highlights

Bridge dynamic deflection is one of the most important indicators to reflect bridge structural abnormality, including the quality, operating state, and stiffness, and further provide obvious feedback on the overall deformation of bridges [1]
Boonyapinyo used the Data-subspace identification (SSI) method to extract the modal parameters of a bridge model excited with the wind, and the results showed that the bridge coupling aerodynamic derivative extracted by the Data-SSI method was closer to the true value than the covariance-driven stochastic subspace identification (COV-SSI) method [19]
(1) As shown in Figure 6a, Rbin 49 and Rbin 19 of the right sub-bridge (RSB) have no valid data remaining between UL and LL after the boxplot, which is presumed to be caused by environmental impact and some other interference sources

Summary

Introduction

Bridge dynamic deflection is one of the most important indicators to reflect bridge structural abnormality, including the quality, operating state, and stiffness, and further provide obvious feedback on the overall deformation of bridges [1]. Compared with the traditional contact transducers, such as piezoelectric accelerometers, optical fiber sensors, strain gauges, and inductance meters, ground-based synthetic aperture radar (GB-SAR), a non-contact measurement technology, can perform all-weather, large-scale, and longdistance dynamic deflection measurement for the monitored bridges [2,3,4]. Structural modal parameters are important indices to reflect the dynamic characteristics of the monitored bridge, which can be identified from the corresponding dynamic deflection. It is of great significance to understand the current characteristics of the monitored bridges with the determined structural modal parameters, which can provide a basis to perform state evaluation and abnormal monitoring of the bridges [7,8]

Methods

Results

Conclusion