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.

This study introduces a simplified method for predicting the optimal cutting conditions to maximize excavation efficiency based on tool forces. A laboratory-scale linear rock-cutting test was conducted using a conical pick on Finike limestone. The tool forces and their ratios were analyzed in relation to cutting parameters such as penetration depth and spacing. While the cutting force (FC) and normal force (FN) increased with the penetration depth and spacing, this relationship could not predict the optimal cutting conditions. The ratio of the mean normal force to the mean cutting force (FN_m/FC_m) increased with the penetration depth and the ratio of spacing to penetration depth (s/d). However, even while including this relationship, predicting optimal cutting conditions remained challenging. The ratio of the peak cutting force to the mean cutting force (FC_p/FC_m) reached a maximum value at a specific s/d, which is similar to the relationship between the specific energy (SE) and s/d. The optimal s/d obtained through the SE methodology was found to be between 3 and 5, and FC_p/FC_m reached a maximum at s/d. The error between the optimal s/d and the s/d in which FC_p/FC_m was maximized was less than 5%. Therefore, it was confirmed that the optimal cutting conditions could be predicted through the relationship between FC_p/FC_m and s/d. Additionally, by using the results from previous studies, the optimal cutting conditions obtained from the SE methodology and the proposed methodology were found to agree within a margin of error of 20%. The proposed methodology can be beneficial for the design of cutter heads and the operation of excavation machines.

Satellite data have emerged as a widely used tool for global research. In particular, satellite data aid in overcoming long-term observational challenges caused by seasonal effects, facilitating comprehensive monitoring of large regions and simplifying infrastructure maintenance. However, in Korea, the application of satellite SAR (Synthetic Aperture Radar) data within the construction sector remains relatively limited. This study used satellite SAR data to analyze the effects of infrastructure (underground roads and subway systems) currently under construction in Korea on the surrounding ground surface, in order to identify risk zones. This study explored methods for continuously monitoring extensive areas over long distances and selecting potential risk zones. The results indicated substantial ground displacements at specific locations and seasonal changes. Furthermore, past and current satellite SAR data were overlapped to facilitate a time-series analysis. The results confirmed that satellite SAR can improve the monitoring of construction projects in Korea and provide valuable information that can contribute to enhancing maintenance efforts, including near-future ground displacement data. The results are expected to help citizens maintain a safe and sustainable life in urban areas.

The long and large-diameter uncharged hole-boring (LLB) method is a cut-blasting method used to reduce vibration induced by blasting. This method typically involves creating an uncharged hole with a 382 mm diameter and drilling 50 m in the tunnel excavation direction at a time. This method is reported to provide relatively good vibration reduction and with high blasting efficiency through short hole blasting compared to traditional cut methods. In this study, an advanced LLB method incorporating deck charge blasting was investigated to improve the blasting efficiency during long hole blasting. Numerical analysis was performed via ANSYS LS-DYNA to investigate the effectiveness of the deck charge technique. In the original LLB method, explosives were used to break the rocks more finely, and the fragmented rocks were concen trated at the end of the blast holes. On the contrary, the modified LLB, in which two-part explosives were loaded into the blast holes, is expected to push the fragmented rocks to the tunnel face more effectively than the original LLB method. Therefore, it is expected that the proposed LLB method combined with a deck charge technique can achieve superior blasting efficiency.

Internet of Things (IoT)-based slope monitoring systems support real-time stability surveillance, but frequent false alarms cause alarm fatigue and delay responses to genuine hazards. We developed a clustering-based framework that classifies alarm causes into three categories—Malfunction, Actual Displacement, and Others—from inclinometer and extensometer data. From 55 alarm events recorded at 43 monitoring stations in 2025, 85 domain-informed features (statistical, waveform, spatial-correlation, trend, sudden-synchronization, and malfunction-pattern) were extracted and compared across five feature-extraction methods, six selection levels, and three clustering algorithms. Because the feature ranking uses expert-assigned labels, the framework is characterized as label-informed feature ranking followed by unsupervised clustering, and it was evaluated with 100 random seeds, nested leave-one-site-out validation, bootstrap confidence intervals, and permutation tests. A compact set of five features—three cross-correlation-based synchronization features and two spatial-correlation features—was stable across seeds and achieved a leave-one-site-out accuracy of 0.80 (95% CI 0.69–0.89) with the full 8 h window. Using only data recorded up to the alarm time, accuracy rose to 0.91 (0.84–0.98), 12 of the 13 Actual Displacement events were recovered (recall 0.92), and no non-displacement event was assigned to the displacement cluster. Synchronization features separated genuine displacement from the other causes, whereas spatial-correlation features distinguished external disturbance from sensor malfunction. The interpretable framework is proposed as a decision-support filter that complements conventional threshold-based alarms.