Prediction of undrained shear strength in soil-cement columns contructed with dry mixing method for Bangkok soft clay | |
| Author | Karki, Bipin Singh |
| Call Number | AIT Thesis no.GE-20-13 |
| Subject(s) | Soil stabilization--Thailand--Bangkok Mixed methods research--Thailand--Bangkok Machine learning--Thailand--Bangkok |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | In the present study, the relationships between cement content, total water-cement ratio, curing time, and natural moisture content with undrained shear strength have been established using machine learning techniques.“Different varieties of cement contents were used to prepare laboratory samples of SCC with curing times of 7, 14, and 28 days. Highway No. 35 at Bang Khun Thain-Ekachai intersection (part1) was another site where the same amount of cement was used to construct SCC. Geotechnical data up to 12 m from the AIT site and Highway No.35 has been considered to develop models and statistical correlations. Multivariate regression (MLR) model, random forest (RF) model, and artificial neural network (ANN) regression model were used for prediction of undrained shear strength. Coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used as performance detectors of different models. R2 values for all models ranged from 0.70 to 0.99 for training data and from 0.5 to 0.94. The linear equation is proposed using cement content, NMC, and CT as the parameters using MLR, and different graphs are presented using RF and ANN models. |
| Year | 2021 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Geotechnical Engineering (GE) |
| Chairperson(s) | Chao, Kuo Chieh; |
| Examination Committee(s) | Avirut Putiwongrak; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2021 |