Skip to main content

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


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Data availability

Data will be made available on request.


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)


Boosting regressor combined with Bayesian optimization


Artificial Intelligence


Machine Learning


Artificial neural network


Support vector regression


Gradient Boosting regression


AdaBoost regressor


Random forest


Composite geomembrane


High-density polyethylene perforated geomembrane


Smooth high-density polyethylene geomembrane


Coarse sand


Crushed stone


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


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


AdaBoost regressor combined with Bayesian optimization


Random forest combined with Bayesian optimization


Grid search


Random search


Mean square error


Root-mean-square error


Mean absolute percentage error


Linear regressor


  1. Abuel-Naga HM, Bouazza A (2014) Numerical experiment-artificial intelligence approach to develop empirical equations for predicting leakage rates through GM/GCL composite liners. Geotext Geomembr 42(3):236–245.

    Article  Google Scholar 

  2. Afzali-Nejad A, Lashkari A, Farhadi B (2018) Role of soil inherent anisotropy in peak friction and maximum dilation angles of four sand-geosynthetic interfaces. Geotext Geomembr 46(6):869–881.

    Article  Google Scholar 

  3. Afzali-Nejad A, Lashkari A, Shourijeh PT (2017) Influence of particle shape on the shear strength and dilation of sand-woven geotextile interfaces. Geotext Geomembr 45(1):54–66.

    Article  Google Scholar 

  4. Araújo GLS, Sánchez NP, Palmeira EM, Almeida M, das G. G. de. (2022) Influence of micro and macroroughness of geomembrane surfaces on soil-geomembrane and geotextile-geomembrane interface strength. Geotext Geomembr 50(4):751–763.

    Article  Google Scholar 

  5. Biabani MM, Indraratna B (2015) An evaluation of the interface behaviour of rail subballast stabilised with geogrids and geomembranes. Geotext Geomembr 43(3):240–249.

    Article  Google Scholar 

  6. Bo Y, Liu Q, Huang X, Pan Y (2022) Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization. Tunn Undergr Space Technol.

    Article  Google Scholar 

  7. Bo Y, Huang X, Pan Y, Feng Y, Deng P, Gao F, Liu P, Liu Q (2023) Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database. Undergr Space 10:91–117.

    Article  Google Scholar 

  8. Cañizal J, Bacas BM, Konietzky H (2015) Frictional behaviour of three critical geosynthetic interfaces. Geosynth Int 22(5):355–365.

    Article  Google Scholar 

  9. Chang JY, Feng SJ (2021) Dynamic shear behaviors of textured geomembrane/nonwoven geotextile interface under cyclic loading. Geotext Geomembr 49(2):388–398.

    Article  Google Scholar 

  10. Chang JY, Feng SJ, Zheng QT, Shen Y (2021) Cyclic shear behavior of GMB/GCL composite liner. Geotext Geomembr 49(3):593–603.

    Article  Google Scholar 

  11. Chao Z, Fowmes G (2021) Modified stress and temperature-controlled direct shear apparatus on soil-geosynthetics interfaces. Geotext Geomembr 49(3):825–841.

    Article  Google Scholar 

  12. Chao Z, Fowmes G, Dassanayake S (2021) Comparative study of hybrid artificial intelligence approaches for predicting peak shear strength along soil-geocomposite drainage layer interfaces. Int J Geosynth Ground Eng 7(3):1–19

    Article  Google Scholar 

  13. Chao Z, Shi D, Fowmes G, Xu X, Yue W, Cui P, Hu T, Yang C (2023) Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation. Geotext Geomembr 51(1):179–198.

    Article  Google Scholar 

  14. Cho G-C, Dodds J, Santamarina JC (2006) Particle shape effects on packing density, stiffness, and strength: natural and crushed sands. J Geotech Geoenviron 132(5):591–602.

    Article  Google Scholar 

  15. Choudhary AK, Krishna AM (2016) Experimental investigation of interface behaviour of different types of granular soil/geosynthetics. Int J Geosynth 2(1):1–11.

    Article  Google Scholar 

  16. Chou JS, Truong DN, Le TL, Truong TH, T. (2021) Bio-inspired optimization of weighted-feature machine learning for strength property prediction of fiber-reinforced soil. Expert Syst Appl 180:115042.

    Article  Google Scholar 

  17. Chou JS, Yang KH, Pampang JP, Pham AD (2015) Evolutionary metaheuristic intelligence to simulate tensile loads in reinforcement for geosynthetic-reinforced soil structures. Comput Geotech 66:1–15.

    Article  Google Scholar 

  18. Debnath P, Dey AK (2017) Prediction of laboratory peak shear stress along the cohesive soil-geosynthetic interface using artificial neural network. Geotech Geol Eng 35(1):445–461.

    Article  Google Scholar 

  19. Dejong JT, Asce AM, Westgate ZJ (2009) Role of Initial state, material properties, and confinement condition on local and global soil-structure interface behavior. J Geotech Geoenviron 135(11):1646–1660.

    Article  Google Scholar 

  20. Dove JE, Asce M, Jarrett JB, Asce SM (2002) Behavior of dilative sand interfaces in a geotribology framework. J Geotech Geoenviron.

    Article  Google Scholar 

  21. Dove JE, David Frost J (1999) Peak friction behavior of smooth geomembrane-particle interfaces. J Geotech Geoenviron 125(7):544–555.

    Article  Google Scholar 

  22. Dove JE, Frost JD (1996) A method for measuring geomembrane surface roughness. Geosynth Int 3(3):369–392.

    Article  Google Scholar 

  23. Eyo E, Abbey S (2022) Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics. Rock Mech Rock Eng 14(2):603–615.

    Article  Google Scholar 

  24. Feng SJ, Shi JL, Shen Y, Chen HX, Chang JY (2021) Dynamic shear behavior of GMB/CCL interface under cyclic loading. Geotext Geomembr 49(3):657–668.

    Article  Google Scholar 

  25. Ferreira FB, Vieira CS, Lopes ML (2015) Direct shear behaviour of residual soil-geosynthetic interfaces - influence of soil moisture content, soil density and geosynthetic type. Geosynth Int 22(3):257–272.

    Article  Google Scholar 

  26. Freund Y, Schapire RE (1997) J Comput Syst Sci (55)

  27. Frost JD, Kim D, Lee SW (2012) Microscale geomembrane-granular material interactions. KSCE J Civ Eng 16(1):79–92.

    Article  Google Scholar 

  28. Gajurel A, Chittoori B, Mukherjee PS, Sadegh M (2021) Machine learning methods to map stabilizer effectiveness based on common soil properties. Transp Geotech 27:100506.

    Article  Google Scholar 

  29. Ghazavi M, Bavandpouri O (2022) Analytical solution for calculation of pull out force-deformation of geosynthetics reinforcing unsaturated soils. Geotext Geomembr 50(2):357–369.

    Article  Google Scholar 

  30. Hang L, Gao YF, He J, Li C, Zhou YD, van Paassen LA (2022) Pullout behavior of biocement-geosynthetic reinforcement system: a parametric study. Acta Geotech 17:5429–5439.

    Article  Google Scholar 

  31. He PF, Mu YH, Ma W, Huang YT, Dong JH (2021) Testing and modeling of frozen clay-concrete interface behavior based on large-scale shear tests. Adv Clim Change Res 12(1):83–94.

    Article  Google Scholar 

  32. He Z, Mo H, Siga A, Zou J (2019) Research on the parameters of nonlinear hyperbolic model for clay-geogrid interfaces based on large scale direct shear tests. Transport Geotech 18:39–45.

    Article  Google Scholar 

  33. Himi M, Casado I, Sendros A, Lovera R, Rivero L, Casas A (2018) Assessing preferential seepage and monitoring mortar injection through an earthen dam settled over a gypsiferous substrate using combined geophysical methods. Eng Geol 246:212–221.

    Article  Google Scholar 

  34. Ismail A, Jeng DS (2011) Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model). Eng Appl Artif Intell 24(5):813–821.

    Article  Google Scholar 

  35. Karir D, Ray A, Kumar Bharati A, Chaturvedi U, Rai R, Khandelwal M (2022) Stability prediction of a natural and man-made slope using various machine learning algorithms. Transp Geotech 34:100745.

    Article  Google Scholar 

  36. Kardani N, Aminpour M, Raja NA, M., Kumar, G., Bardhan, A., Nazem, M. (2022) Prediction of the resilient modulus of compacted subgrade soils using ensemble machine learning methods. Transport Geotech 36:100827.

    Article  Google Scholar 

  37. Lashkari A, Jamali V (2021) Global and local sand-geosynthetic interface behaviour. Geotechnique 71(4):346–367.

    Article  Google Scholar 

  38. Lee KM, Manjunath VR (2000) Soil-geotextile interface friction by direct shear tests. Can Geotech J 37(1):238–252.

    Article  Google Scholar 

  39. Lingsi ML, Dietzii MS (2005) The peak strength of sand-steel interfaces and the role of dilation. Soils Found 45(6):1–14.

    Article  Google Scholar 

  40. Liu Q, Wang X, Huang X, Yin X (2020) Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunn Undergr Space Technol 106:103595.

    Article  Google Scholar 

  41. Liu Y, Deng A, Jaksa M (2019) Failure mechanisms of geocell walls and junctions. Geotext Geomembr 47(2):104–120.

    Article  Google Scholar 

  42. Lopes PC, Lopes MP (2001) Shear behaviour of geosynthetics in the inclined plane test - Influence of soil particle size and geosynthetic structure. Geosynth Int 8(4):327–342.

    Article  Google Scholar 

  43. Lu X, Jordan KE, Wheeler MF, Pyzer-Knapp EO, Benatan M (2022) Bayesian optimization for field-scale geological carbon storage. Engineering.

    Article  Google Scholar 

  44. Mahmoodzadeh A, Mohammadi M, Farid Hama Ali H, Nariman AS, Hashim IH, Gharrib Noori KM (2021) Dynamic prediction models of rock quality designation in tunneling projects. Geotech Transp.

    Article  Google Scholar 

  45. Markou IN (2018) A study on geotextile—sand interface behavior based on direct shear and triaxial compression tests. Int J Geosynth Ground Eng 4(8):1–15.

    Article  Google Scholar 

  46. Meng X, Jiang Q, Han J, Liu R (2022) Experimental investigation of geogrid-reinforced sand cushions for rock sheds against rockfall impact. Transp Geotech 33:100717.

    Article  Google Scholar 

  47. Miranda-Valdez IY, Viitanen L, Intyre J, mac, Puisto, A., Koivisto, J., Alava, M. (2022) Predicting effect of fibers on thermal gelation of methylcellulose using Bayesian optimization. Carbohydr Polym 298(15):119921.

    Article  Google Scholar 

  48. Namjoo AM, Jafari K, Tou h, V. (2020) Effect of particle size of sand and surface properties of reinforcement on sand-geosynthetics and sand-carbon fiber polymer interface shear behavior. Geotech Transp.

    Article  Google Scholar 

  49. Namjoo AM, Baniasadi M, Jafari K, Salam S, Tou M, h, M., Tou h, V. (2022) Studying effects of interface surface roughness, mean particle size, and particle shape on the shear behavior of sand-coated CFRP interface. Transp Geotech.

    Article  Google Scholar 

  50. Pant A, Ramana G (2022) Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model. Geotext Geomembr 50(6):1188–1198.

    Article  Google Scholar 

  51. Peng Y, Ding X, Zhang Y, Wang C, Wang C (2021) Evaluation of the particle breakage of calcareous sand based on the detailed probability of grain survival: an application of repeated low-energy impacts. Soil Dyn Earthq Eng 141:106497.

    Article  Google Scholar 

  52. Pietruszczak S, Mroz Z (2001) On failure criteria for anisotropic cohesive-frictional materials. Int J Numer Anal Methods Geomech 25(5):509–524.

    Article  MATH  Google Scholar 

  53. Puri N, Prasad HD, Jain A (2018) Prediction of geotechnical parameters using machine learning techniques. Procedia Computer Science 125:509–517.

    Article  Google Scholar 

  54. Qi C, Tang X (2018) Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study. Comput Ind Eng 118:112–122.

    Article  Google Scholar 

  55. Raja MNA, Shukla SK (2021) Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique. Geotext Geomembr 49(5):1280–1293.

    Article  Google Scholar 

  56. Rezania M, Javadi AA (2007) A new genetic programming model for predicting settlement of shallow foundations. Can Geotech J 44(12):1462–1473.

    Article  Google Scholar 

  57. Shahin MA, Holger M, R., Jaksa, M. B. (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron 128(9):785–793.

    Article  Google Scholar 

  58. Tan D, Suvarna M, hee Tan, Y., Li, J., Wang, X. (2021) A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing. Appl Energy 291:116808.

    Article  Google Scholar 

  59. Tiwari N, Satyam N (2021) Coupling effect of pond ash and polypropylene fiber on strength and durability of expansive soil subgrades: an integrated experimental and machine learning approach. Rock Mech Rock Eng 13(5):1101–1112.

    Article  Google Scholar 

  60. Tay T, Osorio C (2022) Bayesian optimization techniques for high-dimensional simulation-based transportation problems. Transport RES B-METH 164:210–243.

    Article  Google Scholar 

  61. Vangla P, Latha Gali M (2016) Effect of particle size of sand and surface asperities of reinforcement on their interface shear behaviour. Geotext Geomembr 44(3):254–268.

    Article  Google Scholar 

  62. Vafaei N, Fakharian K, Sadrekarimi A (2021) Sand-sand and sand-steel interface grain-scale behavior under shearing. Geotech Transp.

    Article  Google Scholar 

  63. Wang X, Li Z, Shafieezadeh A (2021) Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models. Eng Struct 236:112142.

    Article  Google Scholar 

  64. Wasti Y, Bahadmr Z, ZdukZguk OG (2001) Geomembrane-geotextile interface shear properties as determined by inclined board and direct shear box tests. In Ymldmz Wasti). Geotext Geomembr 19(1):45–57.

    Article  Google Scholar 

  65. Dongxing W, Jiaye Z, Gang Z (2022) Comprehensive evaluation on magnesium potassium phosphate cement-mineral additive stabilized waste sludge. Mar Georesources Geotechnol.

    Article  Google Scholar 

  66. Xie M, Zheng J, Cao W, Dong X, Yang T, Cui L (2022) Mesoscopic pullout behavior of geosynthetics-sand-clay layered reinforced structures using discrete element method. Acta Geotech 17(6):2533–2552.

    Article  Google Scholar 

  67. Yang W, He J, Liu L, Yang H (2022) Testing the shearing creep of composite geomembranes-cushion interface and its empirical model. Soils Found 62(6):101236.

    Article  Google Scholar 

  68. Yamakage S, Kaneko H (2022) Design of adaptive soft sensor based on Bayesian optimization. Case Studies Environ Eng Sci 6:100237.

    Article  Google Scholar 

  69. Zhang Q, Hu W, Liu Z, Tan J (2020) TBM performance prediction with Bayesian optimization and automated machine learning. Tunn Undergr Space Technol 103:103493.

    Article  Google Scholar 

  70. Zhang W, Wu C, Zhong H, Li Y, Wang L (2021) Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12(1):469–477.

    Article  Google Scholar 

  71. Zheng G, Zhang W, Zhou H, Yang P (2020) Multivariate adaptive regression splines model for prediction of the liquefaction-induced settlement of shallow foundations. Soil Dyn Earthq Eng 132:106097.

    Article  Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dongxing Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Wang, D. Shear behaviors and peak friction angle predictions of three critical geomembrane–soil interfaces. Acta Geotech. (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: