한국지반공학회:학술대회논문집 (Proceedings of the Korean Geotechical Society Conference)
- 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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- Pages.987-997
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- 2008
Levenberg-Marquardt 인공신경망 알고리즘을 이용한 지반공학문제의 적용성 검토
Application of Artificial Neural Network with Levenberg-Marquardt Algorithm in Geotechnical Engineering Problem
- 김영수 (경북대학교 토목공학과) ;
- 이재호 (경북대학교 토목공학과) ;
- 서인식 (경동정보대학 토목공학과) ;
- 김현동 (도화엔지니어링) ;
- 신지섭 (경북대학교 토목공학과) ;
- 나윤영 (경북대학교 토목공학과)
- Kim, Young-Su (Dept. of Civil Engineering, Kyungpook National University) ;
- Lee, Jae-Ho (Dept. of Civil Engineering, Kyungpook National University) ;
- Seo, In-Shik (Dept. of Civil Engineering, Kyungdong college of Techno-information) ;
- Kim, Hyun-Dong (Engineer, Dohwa Engineering) ;
- Shin, Ji-Sub (Dept. of Civil Engineering, Kyungpook National University) ;
- Na, Yun-Young (Dept. of Civil Engineering, Kyungpook National University)
- 발행 : 2008.03.28
초록
Successful design, construction and maintenance of geotechnical structure in soft ground and marine clay demands prediction, control, stability estimation and monitoring of settlement with high accuracy. It is important to predict and to estimate the compression index of soil for predicting of ground settlement. Lab. and field tests have been and are indispensable tools to achieve this goal. In this paper, Artificial Neural Networks (ANNs) model with Levenberg-Marquardt Algorithm and field database were used to predict compression index of soil in Korea. Based on soil property database obtained from more than 1800 consolidation tests from soils samples, the ANNs model were proposed in this study to estimate the compression index, using multiple soil properties. The compression index from the proposed ANN models including multiple soil parameters were then compared with those from the existing empirical equations.