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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines |
Alireza TABARSA1( ), Nima LATIFI2, Abdolreza OSOULI3, Younes BAGHERI4 |
1. Depatrment of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan 49138-15759, Iran
2. Terracon Consultants, Inc., Nashville, TN 37211, USA
3. Civil Engineering Department, Southern Illinois University, Edwardsville, IL 62026-1800, USA
4. Faculty of Engineering, Mirdamad Institute of Higher Education, Gorgan 49166-53989, Iran |
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Abstract This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.
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| Keywords
unconfined compressive strength
artificial neural network
support vector machine
predictive models
regression
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Corresponding Author(s):
Alireza TABARSA
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Just Accepted Date: 03 March 2021
Online First Date: 20 April 2021
Issue Date: 27 May 2021
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