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BK232
In order to support the basic information for designing the soft ground improvement, it is highly recommended to understand the spatial distribution of the geotechnical parameters in the landfill site. Therefore, in the present study, we applied Self-Organizing Map (SOM) to detect the characteristics of spatial distribution of the parameters which have been measured in Songsan Green City. For the purpose, the input dataset for SOM was constructed with the classification by USCS, initial Void ratio, unconfined compression strength, compression index from the stations of 41 in the site. Consequently, the methodology based on the SOM proposed in the present study can be considered that it is highly applicable to detect the spatial distribution of the Parameters and it can be used effectively for the further utilization as a data analysis tool.
Eun-Sang IM. 2014. \u201cEvaluation of the Parameters of SOM Model for Selecting Design Section\u201d. Global Journal of Research in Engineering - E: Civil & Structural GJRE-E Volume 14 (GJRE Volume 14 Issue E3): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
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Total Score: 102
Country: Unknown
Subject: Global Journal of Research in Engineering - E: Civil & Structural
Authors: Eun-Sang Im, Dae-hyeon Kim (PhD/Dr. count: 0)
View Count (all-time): 228
Total Views (Real + Logic): 4520
Total Downloads (simulated): 2283
Publish Date: 2014 08, Fri
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In order to support the basic information for designing the soft ground improvement, it is highly recommended to understand the spatial distribution of the geotechnical parameters in the landfill site. Therefore, in the present study, we applied Self-Organizing Map (SOM) to detect the characteristics of spatial distribution of the parameters which have been measured in Songsan Green City. For the purpose, the input dataset for SOM was constructed with the classification by USCS, initial Void ratio, unconfined compression strength, compression index from the stations of 41 in the site. Consequently, the methodology based on the SOM proposed in the present study can be considered that it is highly applicable to detect the spatial distribution of the Parameters and it can be used effectively for the further utilization as a data analysis tool.
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