Skip to main content
SpringerLink
Log in

Assessment of shear stiffness ratio of cohesionless soils using neural modeling

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

Properly estimating strain-dependent shear stiffness of soils is necessary for accurate analysis of soil-structure interaction and seismic ground response problems during earthquake motions. In this research, an artificial neural network (ANN) model was developed for shear stiffness ratio of cohesionless soils. The input variables in this model are shear strain amplitude (γ), effective confining pressure (σ′ 0 ), mean grain size (D 50 ), and relative density (D r ) and output is shear stiffness ratio (G/G max ). A large experimental database was compiled from available published laboratory cyclic tests. Validation of model was carried out with using centrifuge tests results. Subsequently, sensitivity analysis and model accuracy was conducted. Finally, proposed model has been compared with other researcher’s relationships. The results clearly demonstrate the good performance and capability of the proposed ANN-based model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

G:

Shear stiffness

Gmax :

Shear stiffness at small strains

G/Gmax :

Shear stiffness ratio

D50 :

Mean grain size

Dr :

Relative density

σ′0 :

Effective confining pressure

γ:

Shear strain amplitude

e:

Void ratio

R2 :

Coefficient of determination

MAE:

Mean absolute error

RMSE:

Root mean squared error

Min.:

Minimum

Max.:

Maximum

S.D.:

Standard deviation

N:

Number of data

Xm :

Measured value

Xp :

Predicted value

References

  • Aghaei Araei A, Razeghi HR, Hashemi Tabatabaei S, Ghalandarzadeh A (2010) Dynamic properties of gravelly materials. Sci Iran Trans A Civ Eng 17(4):245–261

    Google Scholar 

  • Alexhander I, Morton H (1993) Neurons and symbols: the staff that mind is made of. Chapman and Hall, London

    Google Scholar 

  • Anderson JA (1995) An introduction to neural networks. A bradford book. MIT, Cambridge

    Google Scholar 

  • Arbib MA (1995) Handbook of brain theory and NN. MIT, Cambridge

    Google Scholar 

  • Baziar MH, Jafarian Y (2007) Assessment of liquefaction triggering using strain energy concept and ANN model: capacity energy. Soil Dyn Earthq Eng 27:1056–1072

    Article  Google Scholar 

  • Brennan AJ, Thusyanthan NI, Madabhushi SPG (2005) Evaluation of shear modulus and damping in dynamic centrifuge tests. J Geotech Geoenviron Eng 131(12):1488–1497

    Article  Google Scholar 

  • Dammala PK, Krishna AM, Bhattacharya S, Nikitas G, Rouholamin M (2017) Dynamic soil properties for seismic ground response studies in Northeastern India. Soil Dyn Earthq Eng 100:357–370

    Article  Google Scholar 

  • Darendeli MB (2001) Development of a new family of normalized modulus reduction and material damping curves. PhD dissertation, University of Texas at Austin, Austin, Texas

  • Dehghani AA, Bahremand AR, Shojaei S (2017) Intelligent estimation of flood hydrographs using an adaptive neuro-fuzzy inference system (ANFIS). Model Earth Syst Environ 3(1):35

    Article  Google Scholar 

  • Ellis GW, Yao C, Zhao R, Penumado D (1995) Stress–Strain modeling of sands using artificial neural networks. J of Geotech Geoenviron Eng 121(5):429–435

    Article  Google Scholar 

  • Fashi FH (2016) Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content. Model Earth Syst Environ 2(4):197

    Article  Google Scholar 

  • Fausett LV (1994) Fundamentals neural networks: architecture, algorithms, and applications. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Galushkin AI (2007) Neural networks theory. Springer, New York

    Google Scholar 

  • Gholami M, Bodaghi A (2017) A robust approach through combining optimized neural network and optimized support vector regression for modeling deformation modulus of rock masses. Model Earth Syst Environ 3(1):22

    Article  Google Scholar 

  • Goto S, Suzuki Y, Nishio S, Oh-oka H (1992) Mechanical properties of undisturbed tone-river gravel obtained by in-situ freezing method. Soils Found 32(3):15–25

    Article  Google Scholar 

  • Hardin BO, Drnevich VP (1972) Shear modulus and damping in soils; measurement and parameter effects. J Soil Mech Found Div 98(SM6):603–624

    Google Scholar 

  • Hardin BO, Kalinski ME (2005) Estimating the shear modulus of gravelly soils. J Geotech Geoenviron Eng 131(7):867–875

    Article  Google Scholar 

  • Haykin S (1994) Neural networks. Macmillan College Publishing Company Inc, New York

    Google Scholar 

  • Ishibashi I, Zhang X (1993) Unified dynamic shear moduli and damping ratios of sand and clay. Soils Found 33(1):182–191

    Article  Google Scholar 

  • Iwasaki T, Tatsuoka F (1977) Effects of grain size and grading on dynamic shear moduli of sands. Soils Found 17(3):19–35

    Article  Google Scholar 

  • Iwasaki T, Tatsuoka F, Takagi Y (1978) Shear moduli of sands under cyclic torsional shear loading. Soils Found 18(1):39–56

    Article  Google Scholar 

  • Jafarian Y, Kermani E, Baziar MH (2010) Empirical predictive model for the vmax/amax ratio of strong ground motions using genetic programming. Comput and Geosci 36(12):1523–1531

    Article  Google Scholar 

  • Jafarian Y, Haddad A, Javdanian H (2014) Predictive model for normalized shear modulus of cohesive soils. Acta Geodyn Geomater 11(1):89–100

    Google Scholar 

  • Jafarian Y, Haddad A, Javdanian H (2015) Comparing the shear stiffness of calcareous and silicate sands under dynamic and cyclic straining. In: 7th International Conference of Seismology and Earthquake Engineering (SEE7), 18 May, Tehran, Iran

  • Jafarian Y, Haddad A, Javdanian H (2016a) Estimating the shearing modulus of Boushehr calcareous sand using resonant column and cyclic triaxial experiments. Modares Civ Eng J 15(4):9–19 (in Persian)

    Google Scholar 

  • Jafarian Y, Javdanian H, Haddad A (2016b) Comparing dynamic behavior of Hormuz calcareous and Babolsar siliceous sands under identical conditions. Bull Earthq Sci Eng 3(3):1–10 (in Persian)

    Google Scholar 

  • Javadi AA, Rezania M, Mousavi Nezhad M (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Comput Geotech 33:222–233

    Article  Google Scholar 

  • Javan K, Lialestani MRFH, Ashouri H., Moosavian N (2015) Assessment of the impacts of nonstationarity on watershed runoff using artificial neural networks: a case study in Ardebil, Iran. Model Earth Syst Environ 1(3):22

    Article  Google Scholar 

  • Javdanian H, Seidali M (2016) Evaluating liquefaction induced lateral spreading. In: 5th International Conference on Geotechnical Engineering and Soil Mechanics, 15 November, Tehran, Iran

  • Javdanian H, Haddad A, Mehrzad B (2012) Experimental and numerical investigation of the bearing capacity of adjacent footings on reinforced soil. Electron J Geotech Eng 17(R):2597–2617

    Google Scholar 

  • Javdanian H, Haddad A, Jafarian A (2015a) Evaluation of dynamic behavior of fine-grained soils using group method of data handling. Transp Infrastruct Eng 1(3):77–92. doi:10.22075/jtie.2015.318

    Google Scholar 

  • Javdanian H, Jafarian Y, Haddad A (2015b) Predicting damping ratio of fine-grained soils using soft computing methodology. Arab J Geosci 8(6):3959–3969

    Article  Google Scholar 

  • Javdanian H, Heidari A, Kamgar R (2017) Energy-based estimation of soil liquefaction potential using GMDH algorithm. Iran J Sci Technol Trans Civ Eng. doi:10.1007/s40996-017-0061-4

    Google Scholar 

  • Keshavarzi A, Omran ESE, Bateni SM, Pradhan B, Vasu D, Bagherzadeh A (2016) Modeling of available soil phosphorus (ASP) using multi-objective group method of data handling. Model Earth Syst Environ 2(3):157

    Article  Google Scholar 

  • Kokusho T (1980) Cyclic triaxial test of dynamic soil properties for wide strain range. Soils Found 20(2):45–60

    Article  Google Scholar 

  • Levenberg K (1944) A method for the solution of certain non-linear problems in least Squares. Q Appl Math 2(2):164–168

    Article  Google Scholar 

  • Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  Google Scholar 

  • Masters T (1993) Practical neural network recipes in C++. Academic, San Diego

    Google Scholar 

  • McCombie P, Wilkinson P (2002) The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis. Comput Geotech 29:699–714

    Article  Google Scholar 

  • Nimtaj A, Javdanian H (2014) Response analysis of layered soil in frequency domain using dynamic matrix. Geodyn Res Int Bull GRIB 2(2):20–33

    Google Scholar 

  • Parsaie A, Najafian S, Shamsi Z (2016) Predictive modeling of discharge of flow in compound open channel using radial basis neural network. Model Earth Syst Environ 2(3):150

    Article  Google Scholar 

  • Ribay ED, Maigre ID, Cabrillac R, Gouvenot D (2004) Shear modulus and damping ratio of grouted sand. Soil Dyn Earthq Eng 24:461–471

    Article  Google Scholar 

  • Roblee C, Chiou B (2004) A proposed geoindex model for design selection of non-linear properties for site response analysis. In: Proc., NSF/PEER Int. Workshop on Uncertainties in Nonlinear Soil Properties and their Impact on Modeling Dynamic Soil Response, University of California at Berkeley, Berkeley, California

  • Rollins KM, Evans MD, Diehl NB, Daily WD (1998) Shear modulus and damping relationships for gravels. J Geotech Geoenviron Eng 124(5):396–405

    Article  Google Scholar 

  • Saxena SK, Reddy KR (1989) Dynamic moduli and damping ratios for monterey No.0 sand by resonant column tests. Soils Found 29(2):37–51

    Article  Google Scholar 

  • Seed HB, Idriss IM (1970) Soil moduli and damping factors for dynamic response analysis. Earthquake Engineering Research Center, EERC, University of California, Berkeley, Report No. 70-10

  • Seed HB, Wong RT, Idriss IM, Tokimatsu K (1986) Moduli and damping factors for dynamic analyses of cohesionless soils. J Geotech Eng 112(11):1016–1032

    Article  Google Scholar 

  • Senetakis K, Anastasiadis A, Pitilakis K, Souli A (2011) Dynamic behavior of sand/rubber mixtures, Part II: effect of rubber content on G/G0-γ-DT curves and volumetric threshold strain. J ASTM Int 9(2):1–12

    Google Scholar 

  • Senetakis K, Anastasiadis A, Pitilakis K (2012) Dynamic properties of dry sand/rubber (SRM) and gravel/rubber (GRM) mixtures in a wide range of shearing strain amplitude. Soil Dyn Earthq Eng 33:38–53

    Article  Google Scholar 

  • Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron Eng 128(9):785–793

    Article  Google Scholar 

  • Smith M (1993) Neural networks for statistical modeling. Van Nostrand Reinhold, New York

    Google Scholar 

  • Stokoe KH II, Hwang SK, Lee NJ, Andrus RD (1994) Effects of various parameters on the stiffness and damping of soils at small to medium strains. In: Proc. Int. Symp. prefailure deformation characteristics of geomaterials, Sapporo, vol 2, Japan, pp 785–816

  • Tatsuoka F, Iwasaki T, Takagi Y (1978) Hysteretic damping of sands under cyclic loading and its relation to shear modulus. Soils Found 18(2):25–40

    Article  Google Scholar 

  • Wagh VM, Panaskar DB, Muley AA (2017) Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Model Earth Syst Environ 3(1):36

    Article  Google Scholar 

  • Wasserman PD (1989) Neural computing theory and practice. Prentice Hall Company, Van Nostrand Reinhold, New York

    Google Scholar 

  • Xenaki VC, Athanasopoulos GA (2008) Dynamic properties and liquefaction resistance of two soil materials in an earthfill dam—laboratory test results. Soil Dyn Earthq Eng 28:605–620

    Article  Google Scholar 

  • Yasuda N, Matsumoto N (1993) Dynamic deformation characteristics of sand and rockfill materials. Can Geotech J 30:747–757

    Article  Google Scholar 

  • Zayani R, Bouallegue R, Roviras D (2008) Levenberg-Marquardt learning neural network for adaptive predistortion for time-varying HPA with memory in OFDM systems. In: 16th European Signal Processing Conference, EUSIPCO, Lausanne, Switzerland

Download references

Acknowledgements

This work has been financially supported by the research deputy of Shahrekord University. The Grant Number was 95GRN1M39422.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Shahrekord University, Shahrekord, Iran

    Hamed Javdanian

Authors
  1. Hamed Javdanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Javdanian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javdanian, H. Assessment of shear stiffness ratio of cohesionless soils using neural modeling. Model. Earth Syst. Environ. 3, 1045–1053 (2017). https://doi.org/10.1007/s40808-017-0351-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-017-0351-7

Keywords

Search

Navigation