• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.4
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• pp.687-699
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• 2018

The construction of subsea tunnel differs from that of inland tunnel because of high water pressure due to sea water level and difficulties to reinforce the ground under construction. Therefore, it is very important to prevent trouble in advance when the subsea tunnel is constructed. In this paper, we established lots of databases about characteristics of geological and mechanical parameters on the construction of subsea tunnel using micro slurry TBM which depth is about 60 m. The correlation analysis is conducted to confirm the effect of thrust, torque and RPM among the excavation database on the net penetration rate. Also, An empirical formula is suggested to predict the net penetration rate through the correlation analysis between FPI (Field Penetration Index) and specific energy from the subsea tunnel excavation database.

• Tunnel and Underground Space
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• v.21 no.1
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• pp.1-10
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• 2011

In these days, the development of optical fiber sensor technology is so remarkable that it can measure various physical and chemical quantities ranging from a few millimeters to over several kilometers. In addition, it is attempted to assess the structural integrity of the state of the advanced technologies and existing structures such as ships, aircrafts, and bridges. This paper introduced the case histories of the measuring technology of optical fiber applied on structures such as roads and tunnels. The case history using OTDR (Optical Time Domain Reflectometery) was also introduced in this paper. Measurement of the pre-convergence of a tunnel is essential to assess the safety of a tunnel and understand the geological conditions ahead of an advancing tunnel. Therefore, the pre-convergence measuring technology using OTDR is expected to substitute conventional measuring techniques.

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

• Journal of the Korean GEO-environmental Society
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• v.24 no.8
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• pp.13-20
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• 2023

Tunnelling through the geological anomalies has widely known to have many difficulties such as bottom heave, crack of lining, squeezing and so on. To stabilize the tunnel during the construction or after construction, various reinforcing methods have been introduced and applied such as micropiling at the bottom of tunnel to prevent the bottom heave. In this study, long-term behavior of tunnel in the presence of geological anomalies was predicted using numerical analyses. To this end, material properties for swelling rock model capable of representing the rock swelling behavior was obtained using matching process with measured data to validate the adopted model. After the model validation, simulations were performed to predict the long-term behavior of tunnel in the geological anomalies.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.643-648
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• 2014

Understanding of fracture networks and rock mass properties during tunnel construction is extremely important for the prediction of dangers during excavation, and for deciding on appropriate excavation techniques and support. However, rapid construction process do not allow sufficient time for surveys and interpretations for spatial distributions of fractures and rock mass properties. This study introduces a new statistical approach for predicting joint distributions at foreside of current excavation face during the excavation process. The proposed methodology is based on a cumulative space diagram for joint sets. The diagram displays the cumulative spacing between adjacent joints on the vertical axis and the sequential position of each joint plotted at equally spaced intervals on the horizontal axis. According to the diagram, the degree of linearity of points representing the regularity of joint spacing; a linear trend of the points indicates that the joints are evenly spaced, with the slope of the line being directly related to the spacing. The linear points which are stepped indicates that the fracture set show clustered distribution. A clustered pattern within the linear group of points indicates a clustered joint distribution. Fractures surveyed from an excavated space can be plotted on this diagram, and the diagram can then be extended further according to the plotted diagram pattern. The extension of the diagram allows predictions about joint spacing in areas that have not yet been excavated. To test the model, we collected and analyzed data during excavation of a 10-m-long tunnel. Fractures in a 3-m zone behind the excavation face were predicted during the excavation, and the predictions were compared with observations. The methodology yielded reasonably good predictions of joint locations.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.3
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• pp.233-243
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• 2008

A tunnel behavior predicted in the investigation and design stage is often different from its actual behavior due to mainly the complexity of ground conditions. In a tunnel construction, therefore, it is necessary to ensure the stability of the tunnel by predicting the behaviors of the ground and the supports through observations and measurements, and modifying immediately excavation and reinforcing methods when necessary. To do so, it is important to be able to predict the final tunnel behavior based on the initial tunnel behavior as early as possible. In this study, the correlations were obtained between the initial and the final convergence by analyzing statistically the convergence measurement data, collected from two domestic road tunnels under construction using NATM. In order to estimate the unknown displacements, occurred during the period between the excavation and the first measurement, two methods were used - one is the method by means of regression analysis using a modified exponential function and the other the method by a simple linear regression analysis using the data measured within the distance from tunnel face equal to the tunnel diameter (D). Finally, the relationships were obtained between the initial and final convergence, including the non-measured displacements estimated from the two different methods, by performing linear regression analyses. The regression analysis results showed that there are clear linear relationships between the initial and final convegence and the difference between the two linear regression equations was not that large for when using the exponential function and the simple linear function to estimate the non-measured displacements.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.453-467
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• 2016

Overall risks that can occur in a shield TBM tunnelling are studied in this paper. Both the potential risk events that may occur during tunnel construction and their causes are identified, and the causal relationship between causes and events is obtained in a systematic way. Risk impact analysis is performed for the potential risk events and ways to mitigate the risks are summarized. Literature surveys as well as interviews with experts were made for this purpose. The potential risk events are classified into eight categories: cuttability reduction, collapse of a tunnel face, ground surface settlement and upheaval, spurts of slurry on the ground, incapability of mucking and excavation, and water leakage. The causes of these risks are categorized into three areas: geological, design and construction management factors. Bayesian Networks (BN) were established to systematically assess a causal relationship between causes and events. The risk impact analysis was performed to evaluate a risk response level by adopting an Analytic Hierarchy Process (AHP) with the consideration of the downtime and cost of measures. Based on the result of the risk impact analysis, the risk events are divided into four risk response levels and these levels are verified by comparing with the actual occurrences of risk events. Measures to mitigate the potential risk events during the design and/or construction stages are also proposed. Result of this research will be of the help to the designers and contractors of TBM tunnelling projects in identifying the potential risks and for preparing a systematic risk management through the evaluation of the risk response level and the migration methods in the design and construction stage.