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http://dx.doi.org/10.7843/kgs.2017.33.6.27

Prediction of Ultimate Bearing Capacity of Soft Soils Reinforced by Gravel Compaction Pile Using Multiple Regression Analysis and Artificial Neural Network  

Bong, Tae-Ho (School of Civil and Construction Engrg., Oregon State Univ.)
Kim, Byoung-Il (Dept. of Civil and Environmental Engrg., Myongji Univ.)
Publication Information
Journal of the Korean Geotechnical Society / v.33, no.6, 2017 , pp. 27-36 More about this Journal
Abstract
Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.
Keywords
Gravel compaction pile; Bearing capacity; Multiple regression analysis; Artificial neural network; Cross validation;
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Times Cited By KSCI : 2  (Citation Analysis)
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