Open AccessPredicting the reduction of embankment pressure on the surface of the soft ground reinforced by sand drain with random forest regression
Author(s) -
Tiep Duc Pham,
Nang Duc Bui,
Tuan Tien Nguyen,
Hieu Chi Phan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/869/7/072027
Subject(s) - consolidation (business) , geotechnical engineering , levee , finite element method , stiffness , regression analysis , geology , environmental science , engineering , structural engineering , mathematics , statistics , accounting , business
The consolidation acceleration of embankment with sand drain has been studied in many researchs and standards. However, the pressure distribution on the surface of soft ground with the appearance of sand drain has not focused. Intuitively, because of the much higher stiffness of sand drain compared to this of soft soil, the stress concentration at the top of sand drain occurred along with the pressure decrease on the surface of soft ground. In this paper, the Finite Element Analysis (FEA) is implemented to obtain a labeled database with inputs are sand drain and soft soil moduluses, diameter of sand drains and distance between them. The predicted variable is the ratio of pressure on the surface of soft ground with and without sand drain (K) obtained based on simulation with Plaxis. Consequently, the developed database used as the input of a machine learning model, the Random Forest Regression (RFR). To the end, observations from FEA reinforced the initial intuition of this phenomenon and a predicting model for K also proposed with Random Forest Regression.