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At present, the research results on the stability and reinforcement range of the subgrade when the high-speed railway traverses the edge of a large goaf are still scarce. Taking Guinan high-speed railway traversing the edge of the mined-out area of the Dapan well as the research object, high-speed rail load transfer depth and height of goaf impact were analyzed, Researching the stability of high-speed railway subgrade and the reinforcement range of goaf. The results show that Mined-out edge cracks more obvious development, High-speed railway subgrade at the edge of the goaf is more harmful than when located directly above the goaf; On the No.1 well goaf, the overlap zone between the height of the caving fissure zone and the depth of influence of the high-speed railway load appears, but the height of the goaf in the extremely soft rock will extend to the surface; The high-speed railway subgrade is mainly affected by the residual deformation of the gob of the Dapan well. At last, the reasonable reinforcement range is given through the finite element numerical calculation.

In order to obtain the dynamic stress of invert and its filling in the high-speed railway tunnel under the vibration load of the train, this paper uses the field measurement method to arrange the resistance strain gauge inside the invert of the Lanzhou–Urumqi high-speed railway Fuchuan tunnel. The dynamic stress of the invert and its filling was measured, and the measurement results were verified by numerical analysis, the results obtained by the two were consistent. The results show that under the action of CRH5 electric multiple units, the dynamic stress time history curve of the invert and its filling has 16 peaks, with V-shape on both sides and W-shape in the middle. When the train load speed is constant, the dynamic stress in the tunnel invert and invert filling decreases with the increase of the depth. The dynamic stress at 0.5 m below the track bed is attenuated by about 23.1% compared to 0.2 m, the dynamic stress at 2.0 m below the track bed is attenuated by 67.5% compared to 0.2 m. Tunnel invert and its filling are the main carriers of train vibration load. The dynamic stress attenuation law of the invert and its filling conforms to the exponential function distribution. Through the data fitting, the calculation formula of the dynamic stress in different depths is given. The research results provide a new method for obtaining the dynamic stress of the invert and its filling in high-speed railway tunnel.

Focusing on the special stratum conditions of upper-soft and lower-hard fissured rock strata, this paper conducts quantitative research and analysis on the factors influencing the ground settlement caused by shield tunnel construction considering the coupled hydromechanical properties and presents a corresponding numerical simulation-artificial neural network-Bayesian network- (NS-ANN-BN-) based method. In this method, with a subway shield tunnel as the engineering context, three numerical models are established, in which the shield tunnel is located in soft strata, the shield tunnel is located in semisoft and semihard strata, or the shield tunnel is located in hard strata. According to the numerical simulation (NS) calculations, the ground settlement values under the three types of strata are 38.96 mm, 10.42 mm, and 3.13 mm, respectively. A radial basis function artificial neural network (RBF-ANN) is used to establish the nonlinear mapping relationship between the stratum parameters and the ground settlement, and the training samples and test samples are generated through NS to train the RBF-ANN. After the training is completed, the accuracy of the neural network meets the requirements. The elastic moduli of coarse sand~gravel sand k3 and moderately weathered rhyolite k4 and the cohesion of moderately weathered rhyolite c4 are selected as the key parameters. A large number of training cases are generated through the RBF-ANN, and the Bayesian network (BN) prior probability is calculated by self-learning. A BN model of ground settlement for shield tunnel construction in the upper-soft and lower-hard fissured rock strata is established. The BN back analysis method is used to quantitatively analyze the influencing factors of the ground settlement. The results show that when the tunnel is located in soft strata, the surface settlement is mainly affected by parameter k3. When the ground settlement increases considerably, the three parameters all have a strong influence. When the tunnel is located in semisoft and semihard strata, the influence of the three parameters on the ground settlement is weak. When the tunnel is located in hard strata, the ground settlement is mainly affected by parameter k4. When the ground settlement greatly increases, parameters k3 and c4 have less influence. When the tunnel is located in strata with different soft-hard ratios, the ground settlement is mainly affected by the elastic moduli of coarse sand~gravel sand and moderately weathered rhyolite. This method can provide a reference for the ground settlement analysis of shield tunnel construction in areas with similar fissured rock strata.

In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into three types: objective factors, subjective factors, and monitoring factors. The grading criteria of the risk rating factors are determined, and a dynamic risk rating system is established. A Bayesian network based on this system is constructed by expert knowledge and historical data. The nodes in the Bayesian network are in one-to-one correspondence with the three types of influencing factors, and the probability distribution is determined. Posterior probabilistic and sensitivity analyses are carried out, and the results show that the main influencing factors obtained by the two methods are basically the same. The constructed dynamic risk assessment model is most affected by the objective factor rating and monitoring factor rating, followed by the subjective factor rating. The dynamic risk rating is mainly affected by the surrounding rock level among the objective factors, construction management among the subjective factors, and arch crown convergence and side wall displacement among the monitoring factors. The dynamic risk assessment method based on the Bayesian network is applied to the No. 3 inclined shaft of the Humaling tunnel. According to the adjustment of the monitoring data and geological conditions, the dynamic risk rating probability of level I greatly decreased from 81.7% to 33.8%, the probability of level II significantly increased from 12.3% to 34.0%, and the probability of level III increased from 5.95% to 32.2%, which indicates that the risk level has risen sharply. The results show that this method can effectively predict the risk level during tunnel construction.