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http://dx.doi.org/10.11112/jksmi.2020.24.3.18

Predictive System for Unconfined Compressive Strength of Lightweight Treated Soil(LTS) using Deep Learning  

Park, Bohyun (시지엔지니어링(주) 설계사업부)
Kim, Dookie (공주대학교 건설환경공학부)
Park, Dae-Wook (군산대학교 토목환경공학부)
Publication Information
Journal of the Korea institute for structural maintenance and inspection / v.24, no.3, 2020 , pp. 18-25 More about this Journal
Abstract
The unconfined compressive strength of lightweight treated soils strongly depends on mixing ratio. To characterize the relation between various LTS components and the unconfined compressive strength of LTS, extensive studies have been conducted, proposing normalized factor using regression models based on their experimental results. However, these results obtained from laboratory experiments do not expect consistent prediction accuracy due to complicated relation between materials and mix proportions. In this study, deep neural network model(Deep-LTS), which was based on experimental test results performed on various mixing conditions, was applied to predict the unconfined compressive strength. It was found that the unconfined compressive strength LTS at a given mixing ratio could be resonable estimated using proposed Deep-LTS.
Keywords
Lightweight Treated Soil(LTS); Artificial Neural Network(ANN); Deep Neural Network(DNN); Deep Learning; Deep-LTS;
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Times Cited By KSCI : 2  (Citation Analysis)
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