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To monitor the changes in the force of the tunnel invert steel bars after the groundwater level changes, field tests were performed to accurately and comprehensively characterize the stress acting on the rebar of a tunnel invert. Changes in stress and temperature were monitored for two layers of rebar (upper and lower) in an actual tunnel invert during its repair. The results showed that the changes in stress followed different paths for the upper and lower layers. After the groundwater is replenished, the maximum tensile stress of the rebar was 17.3 MPa, and the maximum compressive stress was 120 MPa. Major changes in stress were observed 2–6 days after rain. Based on this, the seepage path of groundwater is analyzed. During this period, the compressive stress increased threefold, and the tensile stress increased 9.5-fold. The rebar stress in the tunnel invert followed a Gaussian distribution after stabilizing. Four phases of stress progression are identified and discussed. The results can provide data support and theoretical basis for the treatment of invert floor heave in enriched water tunnel.

The strength reduction finite element method (SRFEM) needs to modify the input shear strength material parameters manually based on various reduction factors, which is trivial. In order to simplify the computation process, the SRFEM can be achieved by using field variables provided by ABAQUS; there-fore, it can be done automatically in software. By deriving the relationship between field variables and reduc-tion factor and getting the corresponding step time t1 when the calculation cannot converge, then it can quan-titatively calculate the stability coefficient of the slope. According to the different decay extent and speed of shear strength parameters c and φ in the process of slope failure, this paper adopted the dual reduction factors finite element method based on field variables for stability analysis of slope, and verified the feasibility of this method in the stability analysis by combining with classic examples, and found that this method not only im-proves computational efficiency but also the calculation accuracy.

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.

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.