Ground settlement occurs because of the surrounding ground behavior during tunnel excavation. A high chance of its occurrence could cause the collapse of buildings; therefore, the accurate prediction and assessment of ground settlement are necessary when structures are concentrated in urban regions. This study leverages Geographic Information Systems (GIS) and 3D modeling to evaluate the effects of tunnel excavation on the ground settlement and damage of buildings along the Mandeok–Centum underground highway in Busan. It integrates the field topography with building data to simulate and visualize construction-induced interactions. Numerical analysis is used to assess the effects of the terrain elevation, building presence, excavation sequences, and lag distance between the twin tunnels on the settlement. The results indicate that high terrain elevation, dense building layouts, and shorter distances between tunnels increase settlement. Furthermore, this study deduces that bidirectional excavation causes a rapid increase in settlement compared with parallel excavation, which is evident from the variations in the inflection points during the excavation process. Finally, this study estimates the damage to buildings and ground settlements and visualizes risk maps using GIS, emphasizing the practicality of 3D modeling based on GIS.

The Tunnel Boring Machine (TBM) method has gained attention as an eco-friendly tunneling technique, effectively reducing noise, vibration, and carbon emissions compared to conventional blasting methods. However, ground settlement and volume loss are inevitable during TBM excavation due to the deformation of the surrounding ground, which may even lead to ground collapse in severe cases. In this study, a Shield TBM model, validated using field data, was employed to perform numerical analyses on parameters such as tunnel diameter, ground elastic modulus, face pressure, and backfill pressure. Based on the simulation results, the influence of each parameter on settlement was evaluated, and a predictive model for estimating maximum settlement was developed. The proposed model was statistically validated using p-value assessment, variance inflation factor (VIF), coefficient of determination (R²), and residual analysis. Furthermore, the prediction model showed high agreement with the field data, yielding a prediction error of 8.25%. This study emphasizes the applicability of verified numerical modeling for accurately predicting ground settlement in Shield TBM tunneling and provides a reliable approach for settlement prediction under varying construction conditions.

In this study, a series of unrelieved cutting tests was conducted to analyze the effect of joints on rock cutting using a conical pick. The tests were performed on jointed rock mass specimens with joint spacings (J_s) of 30, 60, and 90 mm and at cutting depths of 3, 6, 9, and 12 mm. For each case, the distance between the cutting path and the joint plane (d) was varied from 0.1J_s to 0.5J_s. The cutting force decreased as the distance from the joint plane increased but reached the level observed in intact rock at the midpoint between adjacent joint planes (d = 0.5J_s). Regardless of joint spacing, the cutting force reached its minimum when d was between 0.2J_s and 0.3J_s. The rock fragmentation zone extended beyond the joint plane when d was 0.1J_s but became confined within the joint plane from around 0.2J_s to 0.3J_s. These results indicate that the influence of the joint is most pronounced within this range. Three types of crack propagation patterns were observed near the joint plane: (1) cracks that terminate at the joint along the shortest path, (2) cracks that pass through the joint and reach the opposite free surface, and (3) cracks that end at a free surface located just inside the joint plane. These observations suggest that the reduction in cutting force can be attributed to shorter crack propagation paths due to the presence of the joint. This study contributes to a better understanding of the cutting behavior of jointed rock masses when using a conical pick.

New energy absorption devices have been developed for use in net-type debris flow protection systems. The capacity of the anchors in the system needs careful analysis because the anchors play a significant role in transmitting energy from the impacting debris to the ground. In this paper, an effective method is proposed for estimating the anchor capacity based on increasing energy levels and the effect of brake frames in the system. For this purpose, four test cases based on varying impact energy levels were developed and full-scale impact tests were performed. The maximum deformations and tensile forces developed in the system were recorded and analyzed. Thereafter, the performance of the net protection system in the presence and absence of brake frames was numerically analyzed using ABAQUS software, and the stress distribution in various system components was investigated. Based on the results of the full-scale impact tests and numerical analysis, a relationship between the total anchor capacity and impact energy level was established by considering the workability of the U-clips. This study demonstrates that approximately 40% reduced anchor capacity can be safely used for impact energy levels ranging from 50 to 200 kJ when brake frames are used in the system.

Ground settlement induced by shield tunnel boring machine (TBM) excavation is a major geotechnical concern in urban tunneling because it may affect the safety of adjacent structures and underground infrastructure. In this study, machine learning models were developed to predict the maximum settlement induced by shield TBM excavation using a three-dimensional numerical analysis database comprising 320 simulation cases generated from combinations of tunnel diameter (D), ground elastic modulus (E), face pressure (FP), and backfill pressure (BP). Random forest (RF) and extreme gradient boosting (XGBoost) models were developed and compared with an existing regression-based settlement prediction equation. Predictive performance and generalization capability were evaluated using random split and GroupKFold validation techniques. Under random split validation, RF achieved the highest predictive performance, with a coefficient of determination of 0.997 and a root mean square error of 0.438 mm, followed by XGBoost. Both machine learning models outperformed the existing settlement prediction equation. However, model performance decreased substantially under GroupKFold validation, indicating limited generalization capability under unseen D–E grouped conditions. The results demonstrate that the developed machine learning models provide accurate predictions within the range of tunnel–ground conditions represented by the adopted numerical analysis database. The findings highlight the importance of evaluating both predictive performance and generalization capability, particularly when machine learning models developed from numerical analysis databases are applied beyond the conditions represented in the training database.