## 4 Field Application  
The stress state of the surrounding rock mass changes after starting the excavation of the tunnel. Hence, the support design based on the surrounding rock mass parameters obtained in the previous investigation is inappropriate, in particular for a tunnel engineering design with a high buried depth, large span, and highly-developed structural planes. To provide a basis for theoretical reference and design, this paper inverts and validates the surrounding rock mass parameters by integrating the on-site monitoring with a BP neural network. The BP neural network is a typical nonlinear algorithm, composed of the input, output, and several (one or more) hidden layers (Figure 6), and each layer has several nodes. The connection of nodes between layers is represented by the weight. The BP neural network with one hidden layer is the traditional shallow neural network, whereas that containing multiple hidden layers is the deep learning neural network (Figure 7). The core steps for training the BP neural network are illustrated below (Figure 8), in which the solid line represents forward propagation and the dashed line represents back propagation. The forward propagation means that the data (information or signal) are first imported into the input end, delivered along the network direction, and multiplied by the corresponding weight. The products of the input data and the corresponding weights are then summed, and the results are input into the activation function for calculation. Then calculation results are delivered to the next node as the input. The calculation is performed successively until the final output is obtained. Figure 6: Schematic diagram of the BP neural network structure. Note: The weight \(w\) represents the connection strength; the bias \(b\) indicates whether or not the node is easy to activate (threshold). Figure 7: Deep BP neural network structure. Here we summarize the inversion workflow of the creep parameters of the surrounding rock mass, as shown below (Figure 9). The Ganbao tunnel project of the Wenchuan-Maerkang expressway was taken as an engineering case study. It is an ultra-long left-right separated tunnel project (the left tunnel is 4777 m long and the right one is 4796 m long). The longitudinal profile of the tunnel is illustrated in Figure 10, in which the red surrounding rock mass mainly represents the sericite phyllite; the yellow part represents the sericite phyllite interbedded by carbonaceous phyllite and metasandstone; the blue part shows the carbonaceous phyllite and sericite phyllite interbedded by metasandstone; the green part represents the sericite phyllite and carbonaceous phyllite; and the black part indicates the carbonaceous phyllite. The tunnel was excavated using the drilling and blasting method. A large deformation occurs  
Figure 8: Steps for training the BP neural network. Figure 9: Inversion workflow for the creep parameters of the surrounding rock mass. at the mile sections K141 + 540-K141 + 860 and K143 + 485-K143 + 650, which results in the circumferential cracking of multiple points along the initially supported arch and local cracking and spalling of the sidewall and tunnel crown (Figure 11). The monitoring section is located at K143 + 621 and starts monitoring after 12 h of excavation. The overburden near the monitoring section consists of collapsed debris deposits, sericite phyllite and carbonaceous phyllite (from top to bottom, Figure 12),the lithology presented on the face of the surrounding rock mass of the largely-deformed tunnel section (Figure 13) is dominated by the carbonaceous phyllite and sericite phyllite, mainly the Grade-V surrounding rock mass. The rock mass is found with a laminated texture and has a buried depth of 646 m, with a vertical stress of 21.7 MPa and a horizontal stress of 8.3 MPa. Field observations indicate minimal effects of underground water, and thus the correction factor \(\lambda\) for the effects of underground water on the weathering conditions of the rock mass is set as one.