## 1 Introduction
Compared with traditional static methods, better inversion results were achieved, and the phenomenon of \"modulus transferring\" was effectively avoided, but the inversion is not efficient and the results are not accurate. [PERSON] et al. [18] overcame the low search efficiency and heavy calculation workload by applying the conventional particle swarm optimization (PSO) [19] algorithm to the inversion of geotechnical engineering parameters, and proposed an improved PSO algorithm. However, the inversion based on peak deflection was still characterized by the problem of non-unique solutions, while the inversion based on the time-history curve involved a large calculation workload. Moreover, a single objective function cannot reflect the complex nonlinear function optimization problem. The existing inversion algorithms are restricted by various factors. To solve the current challenges, an accurate and effective inversion method is needed. The convolutional neural network (CNN) has a strong feature extraction ability [20; 21]. It is a potential scheme to realize pavement detection based on vibration signals, and can effectively solve the challenges faced by the above research. Due to its unique mechanism of partial connection and weight sharing, the CNN has exceptional advantages in processing massive data. In the field of damage detection based on vibration signals, [PERSON] et al. [22] used a CNN to determine the damage location of a steel structure. Furthermore, [PERSON] et al. [23] presented a novel and efficient approach for detection of structural damage from real-time vibration signals via a CNN. In terms of the prediction of the mechanical properties of materials by CNNs, [PERSON] et al. [24] carried out research on the mechanical properties of hot-rolled steel. To further improve the computational efficiency of the CNN, [PERSON] et al. [25] established a fault diagnosis method for cylindrical roller bearing cages based on vibration signals and a 1-D CNN. These applications all confirm the unique advantages of CNNs in the field of diagnosis and prediction, as well as their potential application prospects for the task of processing pavement vibration signals and the inversion of pavement structural parameters. For this reason, this study develops an accurate and effective inversion algorithm for the parameter identification of multi-layered pavement structures based on a CNN with SEM as the forward calculation model. As an algorithm for solving the dynamic responses of pavement structures, SEM can accurately simulate the dynamic effect of FWD loads and avoid the phenomenon of \"modulus transferring\". In addition, most of the inversion calculations use the maximum value of the deflection as the training samples, which do not reflect the overall dynamic characteristics of the pavement structures. As a result, the inversion results suffer from the non-unique problem, and the calculation accuracy cannot be guaranteed. In this paper, the deflection curves are used to replace the peak values of the deflection curves as the training samples, which can reflect the dynamic characteristics of the pavement structure more comprehensively. The CNN model is used to predict the dynamic modulus and thickness of the pavement structure layers. The proposed inversion algorithm is verified by the assumed numerical model of pavement structures, and relevant parameters in the algorithm are analyzed.