Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
  • Indexed by
  • Korea Citation Index (KCI)
Title Predictive System for Unconfined Compressive Strength of Lightweight Treated Soil(LTS) using Deep Learning
Authors 박보현(Bohyun Park) ; 김두기(Dookie Kim) ; 박대욱(Dae-Wook Park)
Page pp.18-25
ISSN 2234-6937
Keywords 경량혼합토; 인공신경망; 심층신경망; 딥러닝 Lightweight Treated Soil(LTS); Artificial Neural Network(ANN); Deep Neural Network(DNN); Deep Learning; Deep-LTS
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.