Proceedings of the Korean Geotechical Society Conference (한국지반공학회:학술대회논문집)
- 2006.03a
- /
- Pages.1159-1164
- /
- 2006
Application of Artificial Neural Network Theory for Evaluation of Unconfined Compression Strength of Deep Cement Mixing Treated Soil
심층혼합처리된 개량토의 일축압축강도 추정을 위한 인공신경망의 적용
- 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.)
- Published : 2006.03.24
Abstract
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.