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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
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.
School of Resources and Safety Engineering, Central South University, Changsha, China.
Department of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima, Japan.
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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2020 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 12, no 6, article id 2218Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020. Vol. 12, no 6, article id 2218
Keywords [en]
machine learning, random forest, particle swarm optimization, Vietnam
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-78038DOI: 10.3390/su12062218ISI: 000523751400065OAI: oai:DiVA.org:ltu-78038DiVA, id: diva2:1414403
Note

Validerad;2020;Nivå 2;2020-03-16 (johcin)

Available from: 2020-03-13 Created: 2020-03-13 Last updated: 2020-04-29Bibliographically approved

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Al-Ansari, Nadhir

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