• Title/Summary/Keyword: Boring machine

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A Study on the Optimal Setting of Large Uncharged Hole Boring Machine for Reducing Blast-induced Vibration Using Deep Learning (터널 발파 진동 저감을 위한 대구경 무장약공 천공 장비의 최적 세팅조건 산정을 위한 딥러닝 적용에 관한 연구)

  • Kim, Min-Seong;Lee, Je-Kyum;Choi, Yo-Hyun;Kim, Seon-Hong;Jeong, Keon-Woong;Kim, Ki-Lim;Lee, Sean Seungwon
    • Explosives and Blasting
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    • v.38 no.4
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    • pp.16-25
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    • 2020

  • Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.

  • https://doi.org/10.22704/ksee.2020.38.4.016 인용 PDF KSCI