한국지반공학회:학술대회논문집 (Proceedings of the Korean Geotechical Society Conference)
- 한국지반공학회 2006년도 춘계 학술발표회 논문집
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- Pages.1159-1164
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- 2006
심층혼합처리된 개량토의 일축압축강도 추정을 위한 인공신경망의 적용
Application of Artificial Neural Network Theory for Evaluation of Unconfined Compression Strength of Deep Cement Mixing Treated Soil
- Kim, Young-Sang (Division of Ocean Engrg., Yosu National University) ;
- Jeong, Hyun-Chel (Division of Ocean Engrg., Yosu National University) ;
- Huh, Jung-Won (Division of Ocean Engrg., Yosu National University) ;
- Jeong, Gyeong-Hwan (Dong-A Geotechnical Engrg. Co., LTD.)
- 발행 : 2006.03.24
초록
In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.