Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis
Manh Duc Nguyen1, Binh Thai Pham2, *, Tran Thi Tuyen3, *, Hoang Phan Hai Yen4, *, Indra Prakash5, Thanh Tien Vu6, Kamran Chapi7, Ataollah Shirzadi7, Himan Shahabi8, Jie Dou9, *, Nguyen Kim Quoc10, Dieu Tien Bui11
Identifiers and Pagination:Year: 2019
First Page: 178
Last Page: 188
Publisher ID: TOBCTJ-13-178
Article History:Received Date: 26/05/2019
Revision Received Date: 31/07/2019
Acceptance Date: 08/08/2019
Electronic publication date: 30/8/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Consolidation coefficient (Cv) is a key parameter to forecast consolidation settlement of soft soil foundation as well as in treatment design of soft soil foundation, especially when drainage consolidation is used in foundation treatment of soft soil.
In this study, the main objective is to predict accurately the consolidation coefficient (Cv) of soft soil using an artificial intelligence approach named Random Forest (RF) method. In addition, we have analyzed the sensitivity of different combinations of factors for prediction of the Cv.
A total of 163 soil samples were collected from the construction site in Vietnam. These samples at various depth (m) were analyzed in the laboratory for the determination of clay content (%), moisture content (%), liquid limit (%), plastic limit (%), plasticity index (%), liquidity index (%), and the Cv for generating datasets for modeling. Performance of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Correlation Coefficient (R) methods. In the present study, various combinations of soil parameters were applied and eight models were developed using RF algorithm for predicting the Cv of soft soil.
Results of model’s study show that performance of the models using different combinations of input factors is much different where R value varies from 0.715 to 0.822.
Present study suggested that RF model with appropriate combination of soil properties input factors can help in better and accurate prediction of the Cv of soft soil.