Name
Abstract | ||
|---|---|---|
In this study, General Regression Neural Network(GRNN), Artificial Neural Network (ANN), Fully Connected Neural Network (FCNN), Support Vector Regression (SVR) and Linear Regression (LR) models have been implemented in order to predict the composition of soil with respect to the Standard Penetration Test (SPT), and soil depth. The primary focus has been on determining a significant correlation between the soil composition with SPT value and depth. Data sets have been used from ward 14, Mymensingh district of Bangladesh and from a construction project along India-Myanmar border. In this study, 8 types of soil, namely, fine sand, silty clay, clayey silt with fine sand, clayey silt, fine sand with silt, silty fine sand, sandy silt, and rubbish has been classified, and the probability of obtaining the soil type classification has been determined. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IS48319.2020.9199941 | 2020 IEEE 10th International Conference on Intelligent Systems (IS) |
Keywords | DocType | ISBN |
Standard Penetration Test (SPT),General Regression Neural Network (GRNN),Artificial Neural Network,Fully Connected Neural Network (FCNN),Support Vector Regression,Linear Regression | Conference | 978-1-7281-5456-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suparna Biswas | 1 | 0 | 0.34 |
| Tarek Ibne Faysal | 2 | 0 | 0.68 |
| Arshi Siddiqui Promiti | 3 | 0 | 0.34 |
| Md. Sazzad Hossain | 4 | 0 | 0.68 |
| Lubaba Bazlul | 5 | 0 | 0.34 |
| Abdullah Md. Sarwar | 6 | 1 | 0.72 |
| Sayeed Md. Shaiban | 7 | 0 | 0.34 |
| Rashedur M. Rahman | 8 | 1 | 8.15 |