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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 163 - 171 (9page)
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초록· 키워드
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The subsidence over abandoned mines has led to big problems in the local areas. Hence, many attempts have been made to predict subsidence and to check the stability of an abandoned mine. This paper focuses on the development of an artificial neural network (ANN) model to predict mine subsidence, and the verification of prediction ability, based upon the investigation of 247 subsidence areas over 27 abandoned mines in Korea. The analysis is divided into two stages. In stage 1 where the data of 229 cases are used for learning as well as prediction of ANN, it is found that the correct answer percentage of final output values to the actual values shows 99, 95, 100 and 100% for four output parameters of subsidence type, final decision, filling method, and local reinforcement respectively. In stage 2 where the information of 18 other cases is used for prediction after learning with the 229 cases, the correct answer percentage of final output values is 89, 78, 100, and 94% for the four parameters, respectively. Finally, it is thought that this ANN model can be used for the prediction of unknown situations regarding subsidence over an abandoned mine with high reliability.
목차
등록된 정보가 없습니다.
참고문헌 (16)
참고문헌 신청
Ambrozic, T. / 2003 / Prediction of subsidence due to underground mining by artificial neural networks / Computers & Geosciences 29:627~637
Coal Industry Promotion Board / 1997 / A study on the mechanism of subsidence over abandoned mine area and the construction method of subsidence prevention :1~67
Dysart, P. S. / 1990 / Regional seismic event classification at the NORESS array : Seismological measurements and the use of trained neural networks / Bulletin of the Seismological Society of America 80:1910~1933
Finnie, G. J. / 1999 / Using neural networks to discriminate between genuine and spurious seismic events in mines / Pure and Applied Geophysics 154:41~56
Garrett, J. / 1994 / Where and why artificial neural networks are applicable in civil engineering / Journal of Computing in Civil Engineering 8:129~130
Coal Industry Promotion Board / 1997 / A study on the mechanism of subsidence over abandoned mine area and the construction method of subsidence prevention :1~67
Dysart, P. S. / 1990 / Regional seismic event classification at the NORESS array : Seismological measurements and the use of trained neural networks / Bulletin of the Seismological Society of America 80:1910~1933
Finnie, G. J. / 1999 / Using neural networks to discriminate between genuine and spurious seismic events in mines / Pure and Applied Geophysics 154:41~56
Garrett, J. / 1994 / Where and why artificial neural networks are applicable in civil engineering / Journal of Computing in Civil Engineering 8:129~130
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