지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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A evaluation for the strength of rock includes a lot of uncertainty due to existence of discontinuity surface and weakness plain in the rock mass, so essential test results and other data for the resonable strength analysis are absolutely insufficient. Therefore, a analytical technique to reduce such uncertainty can be required. A probabilistic analysis technique has mainly to make up for the uncertainty to investigate the strength of rock mass. Recently, a artificial neural networks, as a more newly analysis method to solve several problems in the existing analysis methodology, trends to apply to study on the rock strength. In this study the unconfined compressive strength from basic physical property values of sedimentary rock, black shale and red shale, distributed in Daegu metropolitan area is estimated, using the artificial neural networks. And the applicability of the analysis method is investigated. From the results, it is confirmed that the unconfined compressive strength of the sedimentary rock can be easily and efficiently predicted by the analysis technique with the artificial neural networks.
목차
등록된 정보가 없습니다.
참고문헌 (12)
참고문헌 신청
Bae G. J. / 1998 / Development of a Neural Network Expert System for Safety Analysis of Structures Adjacent to Tunnel Excavation Sitesn / Journal of Korean Geotechnical Socie
Kim N. S. / 1996 / Estimation of Engineering Properties of Rock by Accelerated Neural Network / Journal of Korean Society for Rock Mechanics 6 : 316 ~ 325
Lee I. M. / 1997 / Permeability Prediction of Rock Mass Using the Artificial Neural Networks / Journal of Korean Geotechnical Society 13 (2) : 77 ~ 89
Lee J. H. / 2001 / A study on the Fracture Mechanism and Elastic parameters of Anisotropic Rocks / 석사 / Graduate School, Kyungpook National University
Moon H. G. / 1993 / Development of Artificial Neural Networks for Stability Assessment of Tunnel Excavation in Discontinuous Rock Masses and Rock Mass Classification / Journ
Kim N. S. / 1996 / Estimation of Engineering Properties of Rock by Accelerated Neural Network / Journal of Korean Society for Rock Mechanics 6 : 316 ~ 325
Lee I. M. / 1997 / Permeability Prediction of Rock Mass Using the Artificial Neural Networks / Journal of Korean Geotechnical Society 13 (2) : 77 ~ 89
Lee J. H. / 2001 / A study on the Fracture Mechanism and Elastic parameters of Anisotropic Rocks / 석사 / Graduate School, Kyungpook National University
Moon H. G. / 1993 / Development of Artificial Neural Networks for Stability Assessment of Tunnel Excavation in Discontinuous Rock Masses and Rock Mass Classification / Journ
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