###### Abstract  
Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. The integration of a Mohr-Coulomb element with the Burgers model in series serves to characterize the yield creep behavior of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilizing randomized creep parameters and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally. tunneling project; creep test; parameter inversion; timing of lining +
Footnote †: journal: Medical  
field of surrounding rocks. Recently, parameter inversion-based trial methods such as the bisection method and golden section method have been introduced. For instance, [PERSON] and [PERSON] [21] proposed an inversion method based on the monitored deformation of surrounding rocks in tunnels. [PERSON] [22] proposed the inversion of the elastic modulus and crustal stress of surrounding rocks in tunnels based on monitored displacements. [PERSON] et al. [23] applied the golden section method to the tunnel rock mass parameter inversion and excavation support calculation based on the in situ monitored displacement. With the development of mathematical algorithms, machine learning and intelligent algorithms such as neural networks have been applied for the parameter inversion of rock masses [24; 25]. Nevertheless, the inversion analysis in the above studies uses parameters based on the monitored deformation (elastic modulus, cohesion, and frictional angle) of rock masses. It considers the additional steric effect caused by the support structure of the working face but not the effect of the creep characteristics of rock masses on deformation. This provides inaccurate inversion results. As the distance of the working face from the monitoring section increases, the steric effect becomes negligible; as a result, the effect of the creep characteristics on deformation becomes prominent, especially in the soft rock mass. The deformation of surrounding rocks correlates with not only the combination, parameters, construction timing, and other aspects of the supporting structure, but also the pouring time of the reinforced concrete lining after the support is installed. Early pouring of reinforced concrete will lead to excessively large pressure in the lining in the later stage, whereas late pouring may induce the failure of the support due to overload. The optimal construction time of the lining can be determined by a numerical simulation that considers the rock mass creep effect. This study investigated a diversion tunnel in a water diversion project in Xinjiang, China. Figure 1 shows the picture in the tunnel under investigation. The tunnel is 283 km in length and 428 m in depth on average, and it was constructed using the open TBM method to a diameter of 7.03 m with an excavation step of 1.8 m. The rocks in XZ42+640-45+000 and XZ66+000-69+132 are mainly black mudstone, with significant creep characteristics, and considerably affect the stability of the surrounding rocks.