Closely spaced modes of bridges estimated by a hybrid time–frequency method using a multi-sensor scanning vehicle: Theory and practice. (1st June 2023) - Yang, Y.B.; Li, Z.; Wang, Z.L.; Liu, Z.; Mo, X.Q.; Qiu, F.Q.
Highlights: Propose the use of a single-axle scanning vehicle with multi sensors to accurately estimate bridge parameters. Process the cross covariances of multi-sensor responses by the combined use of WT and SVD. The proposed technique is capable of accurately identifying closely spaced modes of bridges. Compared with the FFT, the proposed technique is robust against various field factors. Field test is conducted verify the efficacy of the proposed technique. Abstract: A hybrid time–frequency method is proposed for accurately estimating the modal parameters of bridges, including the closely spaced modes, by a single-axle scanning vehicle fitted with multi sensors. The novelty of the method is the combined use of the wavelet transform (WT) and singular value decomposition (SVD) for processing the cross covariances of the multi-sensor responses generated by the scanning vehicle, which allows closely spaced modes to be accurately identified. The procedure of modal parameters identification is first given in detail, including the key issues related to the treatment of closely spaced modes. Then, the feasibility of the proposed method is verified for a numerical case, with its advantage over the fast Fourier transform (FFT) clearly shown. Also, a parametric study is presented for various factors including the vehicle damping, vehicle speed, signal-to-noise ratio (SNR) and pavement roughness, by which the robustness of the method was confirmed. Finally, the method is successfully applied to the field test of a three-span girder bridge in Xiamen, China. By the numerical and field-test results, the proposed method is demonstrated to be of high accuracy in identification of modal parameters of bridges, including the closely spaced modes.