The Open Construction & Building Technology Journal




ISSN: 1874-8368 ― Volume 13, 2019
RESEARCH ARTICLE

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 Communications, Ha Noi, Vietnam
2 University of Transport Technology, Hanoi100000, Vietnam
3 Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Vinh, Vietnam
4 Department of Geography, School of Social Education,Vinh University, Vinh, 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, Vietnam
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 Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
10 Department of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
11 Geographic Information System group, Department of Business and IT, University of South-Eastern Norway, BøiTelemark, N-3800, Notodden, Norway

Abstract

Background:

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.

Objective:

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.

Method:

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:

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.

Conclusion:

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.

Keywords: Consolidation coefficient, Artificial intelligence, Random forest, Vietnam, Soft soil, Mean absolute error.


Article Information


Identifiers and Pagination:

Year: 2019
Volume: 13
First Page: 178
Last Page: 188
Publisher Id: TOBCTJ-13-178
DOI: 10.2174/1874836801913010178

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

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© 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: (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.


Correspondence: Address correspondence to these authors at the University of Transport Technology, and Vinh University, Vietnam; Nagaoka University of Technology, Japan; E-mail: binhpt@utt.edu.vn, tuyentt@vinhuni.edu.vn, hoangphanhaiyen@vinhuni.edu.vn, douj888@gmail.com



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