• Tunnel and Underground Space
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• v.15 no.3 s.56
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• pp.177-184
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• 2005

The Sapaesan tunnel, the longest twin tube tunnel (4km) in Korea with 4 lanes each, is under construction with two years of delayed schedule because of the strong opposition from environmental bodies. Therefore, maximizing the construction efficiency was needed in tunnel project to compensate for time delay. This study includes improvements in the construction of the Sapaesan tunnel such as increasing excavation length and changing excavation sequence. In this paper the system for predicting tunnel face ahead is also introduced. Bulk-Emulsion explosive and Cylinder-Cut method were adopted in tunnel blasting to increase the excavation length. Optimum tunnel excavation step was designed to make up delayed time. Tunnel foe mapping, TSP survey and geological prediction system using computerized jumbo-drill were performed fnr safe construction of long and large twin tube tunnel.

• Tunnel and Underground Space
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• v.19 no.6
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• pp.479-489
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• 2009

Appropriate investigation of ground condition near excavation face in tunnelling is an inevitable process for safe and economical construction. In this study mechanical parameters from drilling process for blasting were investigated for the purpose of predicting the ground condition, especially rock mass strength, ahead of tunnel face. Rock mass strength is one of the most important factors for classification of rock mass and making a decision of support type in underground construction. Several rock specimens which are considered homogeneous and having different strength values respectively were tested by hydraulic drill machines generally used. As a result, penetration rate is fairly related with rock mass strength among drilling parameters. It is also found that penetration rate increases along with the higher impact pressure even under same rock strength condition. It is finally suggested that new prediction method for rock mass strength using percussive pressure and penetration rate during drilling work can be utilized well in construction site.

• 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.

• The Journal of Engineering Geology
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• v.5 no.1
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• pp.1-20
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• 1995

A train overturn accident occurred on March 1993 in the Gupo area, northern part of Pusan, unfortunately had taken a heavy toll of lives and caused a great loss of property as well. The reasons for the subsidence of the basement under the railroads, which presumed to be the main cause of the accident, have been investigated from many different angles, including conventional geotechnical investigation methods. The deduced nuin reasons of the subsidence were: 1. blasting for tunnel excavation (NATM) at about 39 meter under the railroads, and 2. unexpected change of bedrock conditions along the direction of tunnel. But this accident was derived nrranlv from the lack of geological and geotechnical information under railroad area because it was impossible to drill beneath the railroads. This paper introduces a new geophysical survey techniqueseisrnic geotomography, and shows some results of the method applying to investigate the underground structure of the accident area. This method not only overcomes the unfavourable environment which many conventional investigation methods cannot face, but produces an image of underground structure with high resolution. Furthermore, the outputs from geotomogaphic analysis could provide very valuable in-situ basic parameters (like seismic velocities, elastic moduli, etc.) which is essential to the design and construction.