Ground collapse may occur around the tunnel when the cavity caused by groundwater runoff cannot resist the surcharge load. Any cavities or subsidence must be managed to avoid dangerous situations by stabilizing the ground through appropriate remedial measures. Trench and trenchless grouting methods can generally be used for the cavity restoration. The trench method is difficult to properly control the injection range and may cause environmental problems due to grout leakage and damages to the adjacent structures due to grouting pressure. In this study, Membrane-grouting method (MGM) is proposed, which, can be an appropriate trenchless grouting method that fills the void tightly and effectively controls the injection range. This method can be an alternative to eliminating the influence of adjacent structures and environmental pollution by inserting a membrane into the cavity and filling the membrane with grout. The membrane blocks the outflow of grout. In addition, it is easy to control the injection pressure to avoid heaving failure. This paper investigates the principle and application of the MGM using a theoretical method, model test and numerical analysis.

This study investigates Tunnel Boring Machine (TBM) performance prediction by employing discrete event simulation technique, which is a potential remedy highlighting its stochastic adaptability to the complex nature of TBM tunnelling activities. The new discrete event simulation model using AnyLogic software was developed and validated by comparing its results with actual performance data for Daegok-Sosa railway project that Earth Pressure Balance (EPB) TBM machine was used in Korea. The results showed the successful implementation of predicting TBM performance. However, it necessitates high-quality database establishment including geological formations, machine specifications, and operation settings. Additionally, this paper introduces a novel methodology for daily performance updates during construction, using automated data processing techniques. This approach enables daily updates and predictions for the ongoing projects, offering valuable insights for construction management. Overall, this study underlines the potential of discrete event simulation in predicting TBM performance, its applicability to other tunneling projects, and the importance of continual database expansion for future model enhancements.

The process of inspecting and replacing cutting tools in a shield tunnel boring machine (TBM) is called cutterhead intervention (CHI) (Farrokh and Kim 2018). Since CHI is performed by a worker who enters the chamber in TBM, the worker is directly exposed to high water pressure and huge water inflow, especially in areas with high ground water levels, causing health problems for the worker and shortening of available working hours (Kindwall 1990). Ham et al. (2022) proposed a method of reducing the water pressure and water inflow by injecting a grout solution into the ground through the shield TBM chamber, and named it the new face grouting method (NFGM). In this study, the TBM mechanical characteristics including the injection pressure of the grout solution and the cutterhead rotation speed were determined for the best performance of the NFGM. To find the appropriate injection pressure, the water inflow volume according to the injection pressure change was measured by using a water inflow test apparatus. A model torque test apparatus was manufactured to find the appropriate cutterhead rotation speed by investigating the change in the status of the grout solution according to the rotation speed change. In addition, to prove the validity of this study, comprehensive water inflow tests were carried out. The results of the tests showed that the injection pressure equal to overburden pressure + (0.10 ~ 0.15) MPa and the cutterhead rotation speed of 0.8 to 1.0 RPM are the most appropriate. In the actual construction site, it is recommended to select an appropriate value within the proposed range while considering the economic feasibility and workability.

In this study, a series of shaking table model tests were performed to evaluate the dynamic earth pressure acting on pile foundation during liquefaction. The dynamic earth pressure acting on piles were evaluated with depth and pile diameters comparing with excess pore water pressure, it means that the kinematic load effect plays a substantial role in dynamic pile behavior during liquefaction. The dynamic earth pressure acting on pile foundations with mass exhibited significant similarity to those without upper mass. Analyzing the non-fluctuating and fluctuating components of both excess pore water pressure and dynamic earth pressure revealed that the non-fluctuating component has a dominant influence. In case of non-fluctuating component, dynamic earth pressure is larger than excess porewater pressure at same depth, and the difference increased with depth and pile diameter. However, in the case of the fluctuating component, the earth pressure tended to be smaller than the excess pore water pressure as the depth increased. Based on the results of a series of studies, it can be concluded that the dynamic earth pressure acting on the pile foundation during liquefaction is applied up to 1.5 times the excess pore water pressure for the non-fluctuating component and 0.75 times the excess pore water pressure for the fluctuating component.

The power plant is a major infrastructure composed of essential machinery such as Turbine Generators (TG), Heat Recovery Steam Generators (HRSG), etc. Particularly, Combined Heat & Power Plants (CHP) are highly efficient power plants that simultaneously produce heat and electricity. Recently, cases have emerged where railway tunnels are being constructed beneath such power plants due to the underground development of urban rail transportation. Therefore, there is a pressing need to assess the impact of vibrations induced by blasting excavation during the construction of railway tunnels beneath the power plant, as well as the vibrations during railway operation, on the major machinery foundations and structures within the power plant. In this study, criteria for evaluating the vibration impact on key vibration-sensitive structures are summarized, and evaluation standards based on international criteria are established. Based on this, the study examines the vibration impact during the blasting excavation method of NATM tunnels beneath the operational power plant. Furthermore, subsequent railway operation, specifically focusing on the impact of train vibrations on Turbine foundations, Pump foundations, and District Heating pipelines using 3D dynamic numerical analysis. The results indicate that vibration values corresponding to up to 97.3% of the evaluation criteria are derived based on the numerical analysis. However, considering the significance of power plant-related structures, additional measures to reduce vibrations are proposed, including further test blasting, alteration of blasting patterns, reducing the charge per delay, or decreasing advance.

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

For numerous tunnelling projects implemented in urban areas due to limited space, it is crucial to take into account the interaction between the foundation, ground, and tunnel. In predicting the deformation of piled foundations and the ground during twin tunnel excavation, it is essential to consider various factors. Therefore, this study derived a prediction model for pile group settlement using machine learning to analyze the importance of various factors that determine the settlement of piled foundations during twin tunnelling. Laboratory model tests and numerical analysis were utilized as input data for machine learning. The influence of each independent variable on the prediction model was analyzed. Machine learning techniques such as data preprocessing, feature engineering, and hyperparameter tuning were used to improve the performance of the prediction model. Machine learning models, employing Random Forest (RF), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM, LGB) algorithms, demonstrate enhanced performance after hyperparameter tuning, particularly with LGB achieving an R² of 0.9782 and RMSE value of 0.0314. The feature importance in the prediction models was analyzed and P_N was the highest at 65.04% for RF, 64.81% for XGB, and P_CTC (distance between the center of piles) was the highest at 31.32% for LGB. SHAP was utilized for analyzing the impact of each variable. P_N (the number of piles) consistently exerted the most influence on the prediction of pile group settlement across all models. The results from both laboratory model tests and numerical analysis revealed a reduction in ground displacement with varying pillar spacing in twin tunnels. However, upon further investigation through machine learning with additional variables, it was found that the number of piles has the most significant impact on ground displacement. Nevertheless, as this study is based on laboratory model testing, further research considering real field conditions is necessary. This study contributes to a better understanding of the complex interactions inherent in twin tunnelling projects and provides a reliable tool for predicting pile group settlement in such scenarios.

A high-level nuclear waste (HLW) repository is designed for the long-term disposal of high-level waste. Positioned at depths of 500-1000 meters, it offers an alternative to the insufficient storage space for spent fuels, providing a long-term solution. High-level waste emits heat and radiation, causing structural deterioration, including strength reduction and cracks. Therefore, the use of piezoelectric sensors for structural health monitoring is essential for evaluating the safety of the structure over time. Unlike other structures, the HLW repository restricts human access after the disposal of HLW, rendering sensor replacement impossible. Therefore, it is necessary to assess both the lifespan and suitability of sensors under the disposal conditions in the HLW repository. This study employed an accelerated life test (ALT) to assess the sensor's lifespan under disposal conditions. Failure modes, failure mechanisms, and operational limits were analyzed through accelerated stress test (AST). Additionally, the parameters of the Weibull life probability distribution and the Arrhenius accelerated life model were estimated through statistical methods, including the likelihood ratio test, maximum likelihood estimation, and hypothesis testing. Results confirmed that the sensor's lifespan decreases significantly with the increase in the temperature limit of the HLW repository. The findings of this study can be used for improving sensor lifespan through shielding, development of alternative sensors, or lifespan evaluation of alternative monitoring sensors.