Evaluation of index properties affecting the unconfined compressive strength of jet-grouted soil columns for Bangkok soft clay | |
| Author | Sarkar, Partho Kumar |
| Call Number | AIT Thesis no.GE-20-11 |
| Subject(s) | Neural networks (Computer science)--Thailand--Bangkok Grouting (Soil stabilization)--Thailand--Bangkok Clay--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 | The unconfined compressive strength (UCS) is a critical design parameter in the design of jet-grouted piles for ground improvement projects. This paper presents an evaluation of index properties affecting the unconfined compressive strength of jet-grouted soil samples for the Bangkok soft clay. A database containing results of field investigation and laboratory testing for jet-grouted soil samples was created for this evaluation. The jet grouted soil samples were collected using a large diameter jet grouting technique from the MRTA Orange Line and Blue Line projects in Bangkok, Thailand. This study focuses on two parts of analysis: (1) the correlation between the UCS and Modulus of Elasticity (E50) of the jet grouted soil samples and (2) the prediction of the UCS for the jet grouted soil samples using index properties of the soils. A correlation of E50 = 100×UCS adopted by the Japanese Jet Grout Association is commonly utilized in Southeast Asia for the design of jet-grouting projects. This correlation could largely depend on soil types and properties, jet-grouting techniques, and construction procedures. A correlation of E50 = 50×UCS was found in this study for the jet-grouted soil samples collected in the database. The prediction of the UCS for the jet grouted soil samples using index properties of the soils was conducted using the Artificial Neural Network (ANN) model, which is a well-known DM algorithm. Various basic index properties of the soil samples were selected in the ANN model to determine the most critical parameters in the prediction of the UCS. It was concluded that unit weight, grading curve, and plasticity index could be used to reasonably predict the UCS using the ANN model. |
| 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) | Local Government Engineering Department (LGED), Bangladesh; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2021 |