###### Abstract
[PERSON] and others used a genetic algorithm to invert the surrounding rock parameters of a highway tunnel and then took the obtained parameters into the numerical model for a forward analysis. The difference between the results and the measured values was small [13]. In previous studies, some algorithms were mainly used to back-analyze the rock parameters surrounding a tunnel in combination with the actual site displacement, but only relying on the displacement for the back analysis, which has certain limitations, and there have only been a few intelligent back-analysis studies for the stations constructed using the arch cover method. In practical projects, especially under soft upper and hard lower geological conditions, the unique arch cover construction method of large span concealed excavation makes it difficult to predict the mechanical parameters of the surrounding rock. Therefore, an accurate prediction of surrounding rock parameters has a certain guiding role for the station constructed with the arch cover method. Table 1 shows the application research of the Gaussian process machine learning algorithm. After 2009, the application of the Gaussian process in civil engineering increased, mainly in the time series prediction of rock and soil mass deformation during the construction process. There are only a few studies on the algorithm being used for the inversion of rock and soil mechanical parameters. The Gaussian process for machine learning can better solve the problem of supervised learning. Two well-known algorithms, the neural network (ANN) and support vector machine (SVM), do not handle some aspects well. For example, the neural network has certain limitations in dealing with small-sample problems, and the support vector machine is also difficult to use for determining its kernel function and loss function. With the continuous development of machine learning, the GP algorithm has become a focus point in the field of international machine learning, and has been successfully applied to regression classification. Based on the Bayesian learning theory, this algorithm has significant advantages in dealing with small samples, nonlinearity, and other problems, and the GP algorithm has flexible nonparametric inference, adaptive parameter acquisition, and a simple implementation process [14]. This paper takes the Shikui Road Station of the Dalian Metro as the engineering background. First, according to 25 groups of orthogonal design schemes, the forward calculation is carried out through the established three-dimensional FLAC3D numerical model. After the results are obtained, a sensitivity analysis is carried out on the results, the key parameters are found, and 25 groups of displacement stress results are normalized. Then, the Gaussian process differential evolution (GP-DE) algorithm is used to train the 25 groups of normalized orthogonal design results and the measured displacement and stress are taken as the input samples for the inversion to obtain accurate surrounding rock parameters, which intend to provide a theoretical basis and reference value for the optimization design of the station and the guidance of the next step of construction in arch cover construction. The remainder of this article is organized as follows. Section 2 introduces the geological uncertainty in the construction of a large-span arch cover station. Section 3 introduces the station parameter identification method based on the GP-DE arch cover method. Section 4 introduces the application of this method in engineering. Section 5 optimizes and analyzes the project's temporary support removal scheme according to the determined parameters. Section 6 discusses the inversion effect of GP-DE and the influence of differential evolution parameters on the back-analysis effect. Finally, Section 7 concludes.