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Open AccessArticle

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

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Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam
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Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam
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School of Resources and Safety Engineering, Central South University, Changsha 410083, China
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Department of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima 739-527, Japan
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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
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University of Transport and Communications, Hanoi 100000, Vietnam
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Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi 100000, Vietnam
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University of Transport and Technology, Hanoi 100000, Vietnam
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
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Authors to whom correspondence should be addressed.
Sustainability 2020, 12(6), 2218; https://doi.org/10.3390/su12062218
Received: 8 February 2020 / Revised: 11 March 2020 / Accepted: 11 March 2020 / Published: 12 March 2020
(This article belongs to the Special Issue Sustainable Geotechnics—Theory, Practice, and Applications)
Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of the models also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) is superior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, the proposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which can be used for the suitable designing of civil engineering structures. View Full-Text
Keywords: machine learning; random forest; particle swarm optimization; Vietnam machine learning; random forest; particle swarm optimization; Vietnam
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Pham, B.T.; Qi, C.; Ho, L.S.; Nguyen-Thoi, T.; Al-Ansari, N.; Nguyen, M.D.; Nguyen, H.D.; Ly, H.-B.; Le, H.V.; Prakash, I. A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil. Sustainability 2020, 12, 2218.

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