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

Binh Thai Pham, Chongchong Qi, Lanh Si Ho, Trung Nguyen-Thoi, Nadhir Al-Ansari, Manh Duc Nguyen, Huu Duy Nguyen, Hai-Bang Ly, Hiep Van Le and Indra Prakash
Additional contact information
Binh Thai Pham: Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam
Chongchong Qi: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Lanh Si Ho: Department of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima 739-527, Japan
Trung Nguyen-Thoi: Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam
Nadhir Al-Ansari: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
Manh Duc Nguyen: University of Transport and Communications, Hanoi 100000, Vietnam
Huu Duy Nguyen: Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi 100000, Vietnam
Hai-Bang Ly: University of Transport and Technology, Hanoi 100000, Vietnam
Hiep Van Le: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Indra Prakash: Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India

Sustainability, 2020, vol. 12, issue 6, 1-16

Abstract: 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.

Keywords: machine learning; random forest; particle swarm optimization; Vietnam (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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