• Tunnel and Underground Space
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• v.6 no.3
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• pp.197-208
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• 1996

Due to the constraints in pre site-investigation for tunnel, it is essential to redesign the support structures suitable for rock mass conditions such as rock strength, ground water and discontinuity conditions for safe tunnel construction. For the selection of optimum support, it is very important to carry out the rock mass classification and in-situ measurement in tunnelling. In this paper, in a mountain tunnel designed by NATM in hard rock, the selectable system for optimum support has been studied. The tunnel is situated at Chun-an in Kyungbu highspeed railway line with 2 lanes over a length of 4, 020 m and a diameter of 15 m. The tunnel was constructed by drill & blasting method and long bench cut method, designed five types of standard support patterns according to rock mass conditions. In this tunnel, face mapping based on image processing of tunnel face and rock mass classification by RMR carried out for the quantitative evaluation of the characteristics of rock mass and compared with rock mass classes in design. Also, in-situ measurement of convergence and crown settlement conducted about 30 m interval, assessed the stability of tunnel from the analysis of monitoring data. Through the results of rock mass classification and in-situ measurement in several sections, the design of supports were modified for the safe and economic tunnelling.

• Tunnel and Underground Space
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• v.20 no.5
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• pp.325-331
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• 2010

In the present research paper large scale blasting was conducted on two different firing patterns, namely, straight V type and skewed V type pattern on the same sandstone overburden bench with similar explosives. The post-blast fragmentation assessments were made by use of digital imaging technique. The total cycle time of 10 $m^3$ rope shovels was also recorded in the field. The results reveal improvements in the fragmentation and excavator performance results for the blasts fired on skewed V type pattern. The paper discusses the skewed V firing pattern and the reasons for its superior performance vis-$\grave{a}$-vis the straight V type pattern.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.2
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• pp.105-118
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• 2008

Shotcrete is one of the main support materials in tunnelling. Its bonding state on excavated rock surfaces controls the safety of the tunnel: De-bonding of shotcrete from an excavated surface decreases the safety of the tunnel. Meanwhile, the bonding state of shotcrete is affected by blasting during excavation at tunnel face as well as bench cut. Generally, the bonding state of shotcrete can be classified as void, de-bonded, or fully bonded. In this study, the state of the back-surface of shotcrete is investigated using impact-echo (IE) techniques. Numerical simulation of IE technique is performed with ABAQUS. Signals obtained from the IE simulations were analyzed at time, frequency, and time-frequency domains, respectively. Using an integrated active signal processing technique coupled with a Short-Time Fourier Transform (STFT) analysis, the bonding state of the shotcrete can be evaluated accurately. As the bonding state worsens, the amplitude of the first peak past the maximum amplitude in the time domain waveform and the maximum energy of the autospectral density are increasing. The resonance frequency becomes detectable and calculable and the contour in time-frequency domain has a long tail parallel to the time axis. Signal characteristics with respect to ground condition were obtained in case of fully bonded condition. As the ground condition worsens, the length of a long tail parallel to the time axis is lengthened and the contour is located in low frequency range under 10 kHz.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.