This paper proposes a new method for predicting the displacement and internal force of constructed tunnels induced by adjacent excavation with dewatering. In this method, the total excavation-induced additional stress on the constructed tunnel is derived by superposing the additional stresses induced by excavation unloading and dewatering effects. The additional stress induced by unloading effect is calculated using Mindlin’s solution. The additional stress induced by dewatering effect is calculated using the principle of effective stress and the Dupuit precipitation funnel curve. With the beam on elastic foundation method, the total additional stress is then used for calculating the tunnel displacement and internal force caused by adjacent excavation with dewatering. Based on three well-documented case histories, the performance of the proposed method is verified. Moreover, a parametric analysis is also performed to capture the effects of excavation depth, tunnel-to-excavation distance, initial water level, excavation plan view size, and specific yield on the responses of the constructed tunnels. The results indicate that the effect of excavation depth on the tunnel maximum vertical displacement, maximum bending moment, and maximum shear force is more significant at an excavation depth greater than the cover depth of the constructed tunnel. The tunnel maximum vertical displacement, maximum bending moment, and maximum shear force decrease nonlinearly with an increase in the tunnel-to-excavation distance and the initial water level. Among the investigated parameters, the excavation dimension in the tunnel longitudinal direction affects most the tunnel responses. The effect of specific yield on the tunnel displacement and internal force induced by adjacent excavation with dewatering becomes more obvious as increasing the initial water level and excavation depth.

The uplifting behaviors of existing tunnels due to overlying excavations are complex and non-linear. They are contributed to by multiple factors, and therefore, they are difficult to be accurately predicted. To address this issue, an extreme gradient boosting (XGBoost) prediction model based on Bayesian optimization (BO), namely, BO-XGBoost, was developed specifically for assessing the tunnel uplift. The modified model incorporated various factors such as an engineering design, soil types, and site construction conditions as input parameters. The performance of the BO-XGBoost model was compared with other models such as support vector machines (SVMs), the classification and regression tree (CART) model, and the extreme gradient boosting (XGBoost) model. In preparation for the model, 170 datasets from a construction site were collected and divided into 70% for training and 30% for testing. The BO-XGBoost model demonstrated a superior predictive performance, providing the most accurate displacement predictions and exhibiting better generalization capabilities. Further analysis revealed that the accuracy of the BO-XGBoost model was primarily influenced by the site’s construction factors. The interpretability of the BO-XGBoost model will provide valuable guidance for geotechnical practitioners in their decision-making processes.

This research examines the impact of soil heterogeneity on the bridge piles beneath a nearby tunnel excavation using Monte-Carlo stochastic analysis. Sensitivity analysis is specifically used to the variation of stratum range, variation coefficient (COV), and fluctuation distance of the soil Young’s modulus. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of COV on the likelihood of a pile failing. The findings suggest that more consideration should be given to the degree and range of geological parameter variations in the strata surrounding the tunnel. The horizontal and vertical fluctuation distances in this project are predicted to be around 18 m and 4.5 m, respectively. The fluctuation range influences the frequency of low stiffness zones in the soil. Additionally, the variation coefficient has a significant effect on the pile deformation, presenting a positive association. The pile deformation exhibits an increasing tendency in the wake of the growing variation coefficient. More significantly, the increase of the COV will directly lead to a rising failure probability of the pile settlement. According to extensive Monte-Carlo simulation calculations, the simulation results considering the variability of soil parameters have a certain deviation from the deterministic in the perspective of probability statistics. It is quite necessary to attach importance to the soil heterogeneity effects in the pile foundation stability under construction disturbance.

The settlement behavior of composite foundations plays an important role in the serviceability and stability of the subgrade or other infrastructures supporting the foundation. However, in water-rich flood plains, due to the complexity of the soft soil properties, the settlement behavior has not been well understood. The objective of this study is to investigate the effects of various key factors on the settlement behavior of composite foundations with deep mixed piles supporting highway subgrade in water-rich flood plains. The investigated subgrade is in operation, and the vehicle load is taken into account. The G347AH Project is considered in this study. Several typical models for predicting composite foundation settlements are discussed. By performing three-dimensional finite difference analysis, a comparison is made between the settlement behavior of the natural foundation and the composite foundation with deep mixed piles. Based on the single factor sensitivity analysis and the multi-factor orthogonal experimental design, the effects of pile length, pile diameter, pile spacing, pile elasticity modulus, cushion elasticity modulus, and cushion thickness on the composite foundation settlement are captured. It is found that among these factors, the degree of influence of pile length is superior. The composite foundation settlements predicted by the models agree well with the field-monitoring data, with the error being about 8.42% and 6.38%, respectively, at two monitoring sections. The research conducted in this paper can effectively reduce the probability of various settlement-related disasters occurring on highway subgrades in water-rich flood plains. Moreover, the research has important theoretical guidance for design optimization in terms of settlement control of highway subgrades in soft soil areas.