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Development of Improvement Effect Prediction System of C.G.S Method based on Artificial Neural Network  

Kim, Jeonghoon (Dept. of Civil and Environmental Engineering, Hanyang University)
Hong, Jongouk (Dept. of Civil and Environmental Engineering, Hanyang University)
Byun, Yoseph (Dept. of Civil and Environmental Engineering, Hanyang University)
Jung, Euiyoup (DENVER KOREA E&C)
Seo, Seokhyun (DENVER KOREA E&C)
Chun, Byungsik (Dept. of Civil and Environmental Engineering, Hanyang University)
Publication Information
Journal of the Korean GEO-environmental Society / v.14, no.9, 2013 , pp. 31-37 More about this Journal
Abstract
In this study installation diameter, interval, area replacement ratio and ground hardness of applicable ground in C.G.S method should be mastered through surrounding ground by conducting modeling. Optimum artificial neural network was selected through the study of the parameter of artificial neural network and prediction model was developed by the relationship with numerical analysis and artificial neural network. As this result, C.G.S pile settlement and ground settlement were found to be equal in terms of diameter, interval, area replacement ratio and ground hardness, presented in a single curve, which means that the behavior pattern of applied ground in C.G.S method was presented as some form, and based on such a result, learning the artificial neural network for 3D behavior was found to be possible. As the study results of artificial neural network internal factor, when using the number of neural in hidden layer 10, momentum constant 0.2 and learning rate 0.2, relationship between input and output was expressed properly. As a result of evaluating the ground behavior of C.G.S method which was applied to using such optimum structure of artificial neural network model, is that determination coefficient in case of C.G.S pile settlement was 0.8737, in case of ground settlement was 0.7339 and in case of ground heaving was 0.7212, sufficient reliability was known.
Keywords
C.G.S method; Settlement; Numerical analysis; ANN; Optimum structure;
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