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
1 Department of Geotechnical Engineering, University of Transport and Communication, Ha Noi, Viet Nam.
2 University of Transport Technology, Hanoi100000, Vietnam.
3 Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Vietnam
4 Department of Geography, School of Social Education,Vinh University, Vietnam
5 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India.
6 Department of Technology, Hoa Binh Construction Group Joint Stock Company, Ha Noi, Viet Nam.
7 Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
8 Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
9 Public Works Research Institute (PWRI), Japan.
10 Department of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam.
11 Geographic Information System group, Department of Business and IT, University College of Southeast Norway, BøiTelemark, N-3800, Norway.


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 the prediction of the Cv. For this, 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 the RF algorithm for predicting the Cv of soft soil. Results of the model’s study show that the performance of the models using different combinations of input factors is much different where R-value varies from 0.715 to 0.822. The present study suggested that the RF model with an appropriate combination of soil properties input factors can help in better and accurate prediction of the Cv of soft soil.

Keywords: Consolidation coefficient, Artificial intelligence, Random forest, Vietnam.

Abstract Information

Identifiers and Pagination:

Year: 2019
Volume: 13
Publisher Item Identifier: EA-TOBCTJ-2019-13

Article History:

Received Date: 26/05/2019
Revision Received Date: 31/07/2019
Acceptance Date: 31/07/2019
Electronic publication date: 23/08/2019
Collection year: 2019

© 2019 Nguyen et al.

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: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the University of Transport Technology, Hanoi 100000, Vietnam; Tel: +390812538059;