• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.270-279
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• 2012

In this study, greenhouse gas emissions occurring from the construction of Jangbogo Antarctic research station were estimated in terms of material production stages and building stages, respectively. In detail, greenhouse gas emissions during the building stages were estimated in terms of marine transportation, inland transportation, construction equipment utilization, and construction camp operation, respectively. As a result, greenhouse gas emissions from material production stages with life cycle assessment were 8,933 ton (as $CO_{2eq}$), equivalent to the 23.8% of total greenhouse gas emissions from the construction of Jangbogo Antarctic research station, and these results indicate that greenhouse gas emissions occurring from material production stages should not be ignored. During the building stages, greenhouse gas emissions occurring from first year were greater than those from second year due to the increase in fuel consumption of freighter during second year. Additionally, marine transportation compared to inland transportation, construction equipment utilization, and construction camp operation was found to be the greater contributor for greenhouse gas emissions during the building stages. The total greenhouse gas emissions estimated from both material production stages and building stages was 34,486 ton (as $CO_{2eq}$), and greater than those estimated from comprehensive environmental evaluation (CEE) of existing other research stations. This difference is mainly attributed from approximate estimation of greenhouse gas emissions of existing other research stations without considering material production stages.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.1
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• pp.74-84
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• 1998

A doubly resonant ultrasonic transducer has been designed as an attempt to increase the bandwidth of underwater transducers. The dual resonance conditions were accomplished by attaching a single acoustic matching layer on the front face of a Tonpilz transducer consisted of an aluminum head, a piezoelectric ring, a brass tail and a prestress bolt. A modified Mason's model was used for the performance analysis and the design of transducers, and the constructed transducers were tested experimentally and numerically by changing the impedances and thicknesses of the head, tail and matching layers in the water tank. Two distinct resonance peaks in the transmitting voltage response(TVR) of a developed transducer were observed at 34.3 and 40.4 kHz, respectively, with the difference frequency of 6.1kHz and the center frequency of 37.2kHz. The values of TVR at these frequencies were 136.5 dB re $1\;\muPa/V$ at 34.3 kHz and 136.8 dB re $1\;\muPa/V$ at 40.4 kHz, respectively. Reasonable agreement between the experimental results and the numerical results was achieved. From this result, it is expected that the generation of the distinct resonances at any two desired frequencies can be achieved through the proper choice of the matching layer to provide the impedance transformation between the transducer and the medium.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.