###### Abstract
The application of bridge influence line and vibration frequency is promising, but the accuracy and operability of the identification method of quasi-static influence line and vibration frequency in practical operation needs to be investigated. The identification of bridge vibration modal parameters can be divided into two categories: frequency-domain methods and time-domain methods [13]. Most of the frequency-domain methods utilize classical spectral estimation, which is measured using nonparametric methods. Time-domain methods belong in the category of modern spectral analysis, and they are parametric methods. [PERSON] et al. [14] decoupled the power spectral density function of a multi-degree-of-freedom system into a series of single-degree-of-freedom power spectral density functions by singular value decomposition of the power spectral density and subsequently used the peaking method to find the frequency; [PERSON] et al. [15] proposed for the first time an indirect measurement method for bridges based on the response of the vehicle, which identifies the modal frequency of the bridge and has advantages of maneuverability, efficiency, economy, etc. There is no need to close the road, and there is also a stationary operation that realizes the rapid detection of grouped bridges. [PERSON] et al. [16] proposed a method for identifying the modal parameters of bridges under vehicle static, where the identification of vibration modes is replaced by frequency identification and a small number of vibration pickers are used to quickly identify the normalized vibration modes of the bridge mass. The above-proposed methods can obtain modal parameters with high accuracy, but all of them require the use of vibration pickers and expensive equipment. In practical engineering, the response data of a certain cross-section is usually obtained quickly by moving the load of a single heavy vehicle, and the bridge influence line is obtained after conversion [17]. However, the measured influence line is often mixed with bridge vibration and noise fluctuation. [PERSON] et al. [18] proposed a new method of influence-line identification based on an adaptive B-spline-basis dictionary and sparse regularization technique, which constructs the influence line by establishing basis function representations, and the curvature-based adaptive node optimization method, which is integrated with a redundant B-spline basis dictionary to ensure the sparsity of the solution and improve the accuracy of influence-line identification. [PERSON] et al. [19] used Empirical Mode Decomposition (EMD) to eliminate dynamic fluctuations in the response of the bridge induced by high-speed vehicles, established influence-line identification pathology with the help of Tikhonov regularization equations, and constructed the vehicle-information matrix to invert quasi-static influence lines. [PERSON] et al. [20] proposed a new method of mixed Empirical and Variational Modal Decomposition, which avoids the distortion of the Variational Modal Decomposition (VMD) method under the high-speed excitation of the vehicle and improves the stability and accuracy of the recognition of influence lines. There is a mature system for removing dynamic perturbation contained in the time response of the influence line, but the proposed methods require dynamic testing to obtain the fundamental frequency of the bridge vibration as the threshold value for decomposition, which is cumbersome. Therefore, based on the axle-coupling system, this paper proposes a non-contact identification method based on the frequency update of the bridge-operation mode to obtain a quasi-static influence line, which is indirectly identified from the vibration displacement response of the axle coupling without the need to deploy a vibration picker, which shortens the testing time of the inspection vehicle and achieves feasibility verification using engineering examples. The technology roadmap is shown in Figure 1.  
Figure 1: Technical route.