Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Prediction of Consolidation Settlements at Vertical Drain Using Modular Artificial Neural Networks

모듈형 인공신경망을 이용한 연직배수공법에서의 압밀침하량 예측

  • 민덕기 (울산대학교 공과대학 토목공학과) ;
  • 황광모 (울산대학교 공과대학 토목공학과 박사 과정) ;
  • 전형원 (울산대학교 공과대학 토목공학과 석사 과정)
  • Published : 2000.04.01
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Abstract

In this paper, consolidation settlements with time at vertical drain sites were predicted by artificial neural networks. Laboratory test results and field measurements of two vertical drain sites were used for training and testing neural networks. Predicted consolidation settlements by trained artificial neural networks were compared with measured settlements by field instrumentation. To improve the prediction accuracy, modular artificial neural networks were studied. From the results of applying artificial neural networks to the same situation, it was shown that modular artificial neural network model was more accurate for the prediction of the consolidation settlements than the general model.

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References

  1. 한국지반공학회지 v.10 no.1 선행재하 공법 설계를 위한 전문가 시스템 김병일;김명모
  2. 한국지반공학회지 v.15 no.4 유전자 알고리즘 및 인공신경망 이론을 이용한 쏘일네일링 굴착벽체 시스템의 최적설계 김흥택;황정순;박성원;유한규
  3. 한국지반공학회지 v.15 no.1 합리적인 측압계수 결정을 위한 인공신경 전문가 시스템의 개발 문상호;문현구
  4. 한국지반공학회지 v.13 no.2 인공신경망 이론을 이용한 암반의 투수계수 예측 이정학;이인모;조계춘
  5. J. Geo. Eng., ASCE. v.121 no.5 Stress-Strain Modeling of Sands Using Artifical Neural Networks Ellis, G. W.;Yao, C.;Zhao, R.;D. Penumadu
  6. J. Geo. Eng. v.122 no.1 Neural Network Modeling of CPT Seismic Liquefaction Data Goh, Anthony T.C.
  7. J. Geo. Eng., ASCE v.124 no.5 Modular Neural Networks for Predicting Settlements during Tunneling Jingsheng Shi;J. A. R. Ortigao;Junli Bai
  8. Bullet. Math. Biophysics 5 A Logical Calculus of Ideas Immanent in Nervous Activity McCulloch, W.S.;Pitts, W.
  9. Nature v.323 no.9 Learning Representations by Back Propagating Errors Rumelhart, D. E.;Hiton, G. E.;Wiliams, R. J.