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Shear behaviors and peak friction angle predictions of three critical geomembrane–soil interfaces

Acta Geotechnica (2023)Cite this article

Abstract

The interface shear behavior is quite beneficial for explaining the stress–strain response of geosynthetics. A series of interface direct shear tests are carried out between three distinct geomembranes and soils to understand their interfacial characteristics. To better comprehend varying trends of the interface shear parameters, three novel prediction models based on machine learning are further developed for the peak friction angles of geomembrane–soil interfaces. The results show that the shear stress–shear displacement curves are more susceptible to the variations of soils’ relative densities, particularly at the geomembrane–coarse sand interface. A conclusion of engineering significance is that coarse sand (D50 = 0.84 mm) is the most suitable material for the bedding layer of the composite geomembrane under σn ≥ 100 kPa and crushed stone (D50 = 7 mm) performs best in the perforated geomembranes-soil interface among the three types of soils. Upon comparing three predicting models for peak friction angles, a novel conclusion is gained that the gradient boosting regressor combined with Bayesian optimization is the most precise model. Then, a comprehensive formula for the peak friction angles containing normal stress, relative density, mean particle size and mean particle regularity is established.

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Data availability

Data will be made available on request.

Abbreviations

D 50 :

Mean particle size of fill material (mm)

S :

Particle sphericity (dimensionless)

R :

Particle roundness (dimensionless)

ρ :

Mean particle regularity (dimensionless)

r max-in :

The radius of the largest inscribed sphere (mm)

r min-cir :

The smallest circumscribed sphere (mm)

δ p :

Peak friction angles (°)

τ p :

Peak shear strength (N/m2)

τ 50 :

Shear strength at horizontal displacement h = 50 mm (N/m2)

σ n :

Normal stress (N/m2)

D r :

Relative density (%)

k τ :

Interface shear strength ratio (dimensionless)

γ :

Strength loss ratio (dimensionless)

BO-GBR:

Boosting regressor combined with Bayesian optimization

AI:

Artificial Intelligence

ML:

Machine Learning

ANN:

Artificial neural network

SVR:

Support vector regression

GBR:

Gradient Boosting regression

Ada:

AdaBoost regressor

RF:

Random forest

GM1:

Composite geomembrane

GM2:

High-density polyethylene perforated geomembrane

GM3:

Smooth high-density polyethylene geomembrane

SP:

Coarse sand

GP:

Crushed stone

G7:

Crushed stone with the mean particle size (D50) of 7 mm

G28:

Crushed stone with the mean particle size (D50) of 28 mm

BO-Ada:

AdaBoost regressor combined with Bayesian optimization

BO-RF:

Random forest combined with Bayesian optimization

GS:

Grid search

RS:

Random search

MSE:

Mean square error

RMSE:

Root-mean-square error

MAPE:

Mean absolute percentage error

LR:

Linear regressor

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52079098), the Fundamental Research Funds for the Central Universities (Wuhan University) (No. 2042022kf1219) and Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences (Grant No. SKLGME021004), the Key Research and Development Program of Wuhan City (No.2022022202015074).

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Authors and Affiliations

  1. School of Civil Engineering, Wuhan University, 8 Donghu South Road, Wuhan, 430072, China

    Yanfang Feng & Dongxing Wang

  2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Dongxing Wang

  3. Key Laboratory of Hydraulic Rock Mechanics of Ministry of Education, Wuhan University, Wuhan, 430072, China

    Dongxing Wang

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Feng, Y., Wang, D. Shear behaviors and peak friction angle predictions of three critical geomembrane–soil interfaces. Acta Geotech. (2023). https://doi.org/10.1007/s11440-023-02082-1

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