NDLI: Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Multiple Regression Analysis and Deep Learning

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Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Multiple Regression Analysis and Deep Learning

Content Provider	MDPI
Author	Bong, Taeho Kim, Sung-Ryul Kim, Byoung-Il
Copyright Year	2020
Description	Aggregate piers have been widely used to increase bearing pressure and reduce settlement under structural footings. The ultimate bearing capacity of aggregate pier-reinforced ground is affected by the soil strength, replacement ratio of piles, and construction conditions. Various prediction models have been proposed to predict the ultimate bearing capacity. However, existing models have shown a broad range of bias, variation, and error, and they are at times unsuitable 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 aggregate piers, and the number and type of the most efficient input variables were evaluated to build a robust predictive model. Accordingly, a multiple regression equation for predicting the ultimate bearing capacity was proposed, and a sensitivity analysis was conducted to identify the effect of input variables. In addition, a deep neural network was applied to estimate the ultimate bearing capacity. The optimal structure was selected on the basis of cross-validation results to prevent overtraining. Prediction errors for two approaches were evaluated and then compared with those of existing models.
Starting Page	4580
e-ISSN	20763417
DOI	10.3390/app10134580
Journal	Applied Sciences
Issue Number	13
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2020-07-01
Access Restriction	Open
Subject Keyword	Applied Sciences Geological Engineering Aggregate Pier Bearing Capacity Multiple Regression Analysis Deep Neural Network Sensitivity Analysis
Content Type	Text
Resource Type	Article

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