## 1 Introduction  
With its construction technology and experience, China has become the world's largest tunnel-building nation [1]. Large cross-section tunnels have been widely adopted in highway tunnels because of their simplicity, high cost-effectiveness, and mechanized construction with the drill and blast method [2]. During the large cross-section tunnel construction, challenges include poor geological conditions, support structure parameters, and effects on the surrounding rocks [3, 4]. To overcome all of these difficulties, artificial intelligence (AI) technology has been widely adopted in the tunnel construction process, specifically in the area of AI for poor geological prediction [5], disaster risk evaluation [6], construction decision-making [7], and deformation prediction [8]. Under tunnel construction in complex construction conditions, the values and the times of the structure deformation can directly reflect the stability of the surrounding rock and support structure. During tunnel construction, the monitoring of the structure deformation is the simplest, most direct, and most widely used for evaluating construction safety [9]. Therefore, effectively obtaining the deformation of tunnel structures is of great significance for evaluating the convergence deformation value and time, which is useful for ensuring safety and determining construction parameters. For the analysis of the convergence deformation, different scholars have carried out lots of corresponding studies. These can be categorized as experimental, theoretical analysis, numerical simulation, and artificial intelligence (AI) based on their methodology. Tunnel deformation data have high research value and are easily accessible. In terms of modeling tests and field tests, different scholars have studied the deformation mechanisms and influencing factors of tunnels surrounding rocks and structures. [PERSON] et al. [10] carried out research on the structural deformation and large deformation damage of tunnels, relying on the Huangjiazhai Tunnel and mineral composition testing and microstructure testing, revealing the microcosmic reasons for the decrease in strength and deformation resistance of the tunnel surrounding rock after water seepage. The deformation of mountain tunnels can be divided into three stages: a rapid deformation stage, a continuous growth stage, and a slow growth stage, followed by a stabilization of the deformation of the tunnel and supporting structures [11; 12]. Based on the construction data of 103 mountain tunnels in China, [PERSON] et al. [13] systematically studied the deformation of the surrounding rock with the stability grade of the surrounding rock. By integrated on-site monitoring of tunnel peripheral rock pressure, concrete stress, steel arch stress, anchor stress, and peripheral rock stratum displacement, [PERSON] et al. [14] revealed the evolution law of the support structure over time. Nevertheless, complex engineering geological conditions are easily neglected in theoretical analysis, and it is difficult to achieve satisfactory results in engineering applications. With the development of basic science, several scholars have carried out research on deformation mechanisms and prediction [15; 16; 17], resulting in constitutive models, plasticity, and damage and viscoplasticity theory. With the development of numerical simulation methods and computer technology, numerical simulation methods have become an important means of the computational analysis of engineering problems with low cost and high efficiency. Numerical methods have been applied to the study of tunnel deformation and stability analysis, and many research results have been achieved [18; 19; 20]. The numerical calculation accuracy depends on the selection of the ontological model and its parameters. In addition, the constitutive model is also the key to numerical simulation, and the classical elastic-plastic model or its improved model is often used to simulate the deformation characteristics of the rock mass [21; 22; 23].