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.

The main contribution of this study is to provide a new idea to detect bridge damage by using PSP-InSAR technology. A most-unfavorable-condition method is proposed for bridge-damage detection and analysis. The method can determine the specific damaged location and occurrence time by using the differential deformation values of persistent scatterer (PS) points on bridge piers. Taking Beijing Suzhou Bridge as an experimental area, 96 COSMO-SkyMed time-series SAR images were used from September 2011 to November 2017. Deformation values of PS points around Suzhou Bridge were acquired and analyzed. Experimental results show that in July 2017, the unusual maximum differential deformation value was 25.73 mm. It occurred between piers D₃ and D₄ of Suzhou Bridge, and it was deduced that the main girder between piers D₃ and D₄ may have been damaged in July 2017. As a validation, taking the differential deformation value between piers D₃ and D₄ as an input, the maximum tensile stress, and the maximum compressive stress were calculated as 2.1 MPa and 8.4 MPa, respectively, through a finite element model. The tensile stress exceeded the design value of the concrete, further confirming the damage of the girder between piers D₃ and D₄. Moreover, all results are consistent with the Suzhou Bridge damage information shown in existing records, which verify the accuracy and reliability of the proposed method.

Spherical reticulated shell structure is an important structural form of large-span space buildings. It is of great significance to monitor three-dimensional (3D) dynamic responses of spherical reticulated shell structure to better understand its seismic performances, which will be helpful in the future to ensure the healthy condition of large-span space buildings during their lifespan. In this study, with the advantages of non-contact and high accuracy, a high-speed videogrammetric measurement method is proposed for monitoring the 3D dynamic responses of the seismically isolated, spherical, reticulated shell structural model. Two issues—the high-speed videogrammetric acquisition system and network configuration, as well as image sequence target tracking and positioning—are emphasized to achieve a cache of high-speed images and to improve the accuracy of tracking and positioning target points. The experimental results on the shaking table from the proposed method have been compared with those from traditional Optotrak Certus and accelerometers. The results prove that the proposed method is capable and useful for analyzing the seismic performance of spherical reticulated shell structures, as the dynamic responses monitoring accuracy of the method can reach the submillimeter level, with root mean square error values of 0.32 mm, 0.7 mm and 0.06 mm in the X, Y and Z directions, respectively.

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment.

The Poynting vector is a powerful tool for calculating the propagation directions of a seismic wavefield, and it has a wide range of applications in reverse time migration. However, an instability issue commonly arises while calculating the Poynting vector. The Poynting vector is a product of the temporal and spatial derivatives of the wavefield. The two derivatives are equal to zero at the local extrema of the seismic wavefield, so the Poynting vector cannot provide the propagation directions at these points. Stabilizing techniques, such as smoothing, optical flow (OF) methods, and time-shifting methods, can be applied to address this issue. However, each of these three types of methods comes with trade-offs. Smoothing is easy to implement but has a low angular resolution, whereas the OF and time-shift techniques have high angular resolutions but are computationally inefficient. We have developed a new method that achieves high resolution and high computational efficiency. Based on the fact that a seismic wavefield and its first-order temporal derivative have the same direction of propagation, and that the unstable points of their Poynting vectors are at different locations, we use the first-order temporal derivative of the seismic wavefield to stabilize its Poynting vector calculation. Our method is nearly as accurate as the OF and time-shift techniques and is more computationally efficient than the smoothing technique. Finally, we use numerical simulations to verify the effectiveness of our method.