###### Abstract
[[https://doi.org/10.3390/app12020683](https://doi.org/10.3390/app12020683)]([https://doi.org/10.3390/app12020683](https://doi.org/10.3390/app12020683))  
A characterization method for pavement structural conditions based on the distribution parameter of the vehicle vibration signal. Department of the Vehicle Vibration Signal. _Appl. Sci._**2002**, 12, 6888. [[https://doi.org/10.3390/app](https://doi.org/10.3390/app)]([https://doi.org/10.3390/app](https://doi.org/10.3390/app))In 1977, [PERSON] [7] proposed a method in his doctoral dissertation to compare the error between the two deflections was minimal. Later, many researchers adopted a similar idea to invert the structural layer modulus based on the falling weight deflectometer (FWD) deflection basin data. As time went by, some scholars employed advanced analytical techniques such as neural networks, genetic algorithms to solve the non-uniqueness of the solution in modulus inversion. [PERSON] and [PERSON] [8] developed an artificial neural network and genetic algorithm (ANN-GA) method to back-calculate the layer moduli of flexible pavement from the falling weight deflectometer (FWD) test. The back-calculated parameters can be directly applied to the mechanistic-empirical design for pavement overlays. Deflection data analysis can effectively evaluate the performance of pavement structure, but this kind of method requires parking before testing. Closing roads will affect traffic flow; therefore, it is difficult to apply on roads with heavy traffic. This study explores the feasibility of a non-stop pavement structure assessment method by analyzing vehicle vibration data. Pavement condition includes structural condition and surface condition. Pavement condition surveys based on vehicle vibration primarily focus on surface performance [9; 10], including pavement roughness [11], surface stresses such as potholes and transverse cracks, etc. [12; 13; 14]. Still, there are very few studies on pavement structures. In fact, the vibration of a vehicle running on the pavement can reflect structural conditions to a certain extent, and only this relationship may be implicit. Inspired by vibration-based structural health monitoring, [PERSON] and co-workers demonstrated for the first time the feasibility of analyzing a bridge structure through the vibration signals of the driven vehicle [15]. Over the past years, they still focused on this issue and have conducted a series of analyses, from the initial extraction of the first-order frequency to the determination of modal parameters such as multi-order frequencies, damping, and mode shapes, then to damage identification and location, and expansion joint detection, etc. [16; 17; 18; 19]. Unlike traditional SHM, these methods are called indirect SHM (ISHM) because the sensors are installed on a moving vehicle rather than on the structure itself [20]. ISHM has the merits of being mobile, economic, and efficient [19]. In response to the problem that the manually extracted features are not very sensitive to damage, [PERSON], et al. [21] proposed a physics-guided algorithm to extract an effective feature to determine the damage in a bridge. With the rapid development of smartphones, in recent years, there has been some research on ISHM, based on the data obtained by the built-in sensors of smartphones [22; 23]. These data are mainly derived from a triaxial accelerometer, a global positioning system (GPS), and an inertial measurement unit (IMU). This kind of method has an even lower cost and can collect more data. By using a smartphone, [PERSON], et al. [1] explored the feasibility of using bicycles to extract common modal parameters of structures, namely the natural frequency modal shape and the operating deflection shape (ODS), which has been demonstrated on a footbridge in Italy. From the above analysis, it can be seen that the research objects of the vehicle-vibration-based method mainly focus on the bridge structure or the pavement surface condition.