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

Document identifier: oai:DiVA.org:ltu-78038
Access full text here:10.3390/su12062218
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Machine learning, Random forest, Particle swarm optimization, Vietnam, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 15 Life on land
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

Determination of shear strength of soil is very important in civilengineering 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 softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped 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 (%). Inthis 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. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable designing of civil engineering structures.

Authors

Binh Thai Pham

Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh, Vietnam.Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh, Vietnam
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Chongchong Qi

School of Resources and Safety Engineering, Central South University, Changsha, China
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Lanh Si Ho

Department of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan
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Trung Nguyen-Thoi

Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh, Vietnam.Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh, Vietnam
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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Manh Duc Nguyen

University of Transport and Communications, Hanoi, Vietnam
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Huu Duy Nguyen

Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam
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Hai-Bang Ly

University of Transport and Technology, Hanoi , Vietnam
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Hiep Van Le

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
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Indra Prakash

Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India
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