###### Abstract
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**[PERSON] 1 State Key Laboratory of Geohazard Prevention and Geoenvinment Protection, College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China; xchenchaox@126.  
investigated patterns of stability [7], deformation [8; 9; 10], mechanical behavior [11], and mechanical properties [12]. The main triggers for large tunnel deformation through highly-dipped laminated soft rock under high in situ stress are high structural stress, unfavorable rock occurrences, and low rock strength [13]. On-site tunnel monitoring has shown that the deformation-failure zone of the surrounding rock mass of laminated soft rock tunnels is concentrated along the direction perpendicular to the rock bedding, instead of the direction of the maximum principal stress; the local deformation of laminated soft rock tunnels is affected by the topography, rock mass structure, and in situ stress [14]. Four mechanisms have been proposed to explain large tunnel deformation, namely, along-bedding sliding, flexural deformation, toppling deformation, and plastic extrusion. For each mechanism, a specific anchoring support plan should be developed [15]. Numerical simulation has been performed to comprehensively analyze the stress environment, the deviator stress distribution [16; 17; 18; 19], and the plastic zone distribution [20; 21] of the surrounding rock mass during tunnel excavation. Such simulations, incorporating the rock characteristics of deterioration, stabilization, and accelerated creep [22], reveal the characteristics of the surrounding rock mass failure with varied surrounding rock mass strengths, buried depths, and roof strengths [23], which provide guiding values to support the design of the analogous tunnels. Due to the complexity of geology, geotechnical materials are typically characterized by discontinuity and heterogeneity, and the rheological parameters of the actual surrounding rock mass of the tunnel are often hard to measure. There are two main methods to obtain creep parameters: one is to calculate creep parameters through laboratory testing of rock combined with a creep constitutive model, but there is a large deviation between the laboratory results for rock and on-site rock mass parameters. Second, to obtain the mechanical parameters of the surrounding rock mass through displacement back analysis based on monitoring data [24]. [PERSON] [25] proposed a novel back analysis program based on a BP neural network, which can realize automatic correction and the adjustment of parameters and adapt to most tunnel projects. [PERSON] [26] introduced a support vector machine (SVM) and an artificial neural network (ANN) to predict tunnel surrounding rock mass displacement, and compared and analyzed the results of the two methods. [PERSON] [27] established a tunnel risk assessment model by combining a fuzzy method with a BP neural network based on historical data from 50 tunnels. Numerical simulation has been widely combined with back-propagation (BP) neural network analysis to calculate the physical and mechanical parameters of surrounding rock mass, which has achieved good application performance [28; 29; 30]. This paper proposed a solution for obtaining the creep parameters of the surrounding rock mass in highway tunnels after excavation. First, the long-term strength of the engineering rock mass was obtained using the Hoek-Brown failure criterion, and a creep constitutive model for the rock mass was developed. Then, back-propagation analysis was performed using the on-site monitoring data to obtain the initial values of the creep parameters of the tunnel's surrounding rock mass. Subsequently, training samples were generated via orthogonal experiments. Then, a BP neural network and numerical simulation were integrated to inversely calculate the tunnel's surrounding rock mass creep parameters that matched the reality.