249
Views
17
CrossRef citations to date
0
Altmetric
Original Articles

Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

, ORCID Icon, , , ORCID Icon & ORCID Icon
Pages 945-962 | Received 27 Jul 2018, Accepted 11 Jan 2019, Published online: 23 Feb 2019

References

  • Mola-Abasi H, Kordtabar B, Kordnaeij A. Parameters controlling strength of zeolite–cement–sand mixture. Int J Geotech Eng. 2017;11:72–79.
  • Ivakhnenko AG. Polynomial theory of complex systems. IEEE Trans Syst Man Cybern. 1971;1:364–378.
  • Mohammadzadeh D, Bazaz JB, Alavi AH. An evolutionary computational approach for formulation of compression index of fine-grained soils. Eng Appl Artif Intell. 2014;33:58–68.
  • Haeri SM, Hamidi A. Constitutive modelling of cemented gravelly sands. Geomech Geoengin an Int J. 2009;4:123–139.
  • Amini Y, Hamidi A. Triaxial shear behavior of a cement-treated sand-gravel mixture. J Rock Mech Geotech Eng. 2014;6:455–465.
  • Kutanaei SS, Choobbasti AJ. Triaxial behavior of fiber-reinforced cemented sand. J Adhes Sci Technol. 2016;30:579–593.
  • Wu P, Houben LJM, Scarpas A, et al. Stiffness modulus and fatigue properties of cement stabilized sand with use of a synthetic modified-zeolite additive. 2015 Annual Meeting of Transportation Research Board. 2015.
  • Salamatpoor S, Jafarian Y, Hajiannia A. Physical and mechanical properties of sand stabilized by cement and natural zeolite. Eur Phys J Plus. 2018;133:205. https://doi.org/10.1140/epjp/i2018-12016-0.
  • Poon CS, Lam L, Kou SC, et al. A study on the hydration rate of natural zeolite blended cement pastes. Constr Build Mater. 1999;13:427–432.
  • Canpolat F, Yılmaz K, Köse MM, et al. Use of zeolite, coal bottom ash and fly ash as replacement materials in cement production. Cem Concr Res. 2004;34:731–735.
  • Yılmaz B, Uçar A, Öteyaka B, et al. Properties of zeolitic tuff (clinoptilolite) blended Portland cement. Build Environ. 2007;42:3808–3815.
  • Mola-Abasi H, Shooshpasha I. Influence of zeolite and cement additions on mechanical behavior of sandy soil. J Rock Mech Geotech Eng. 2016;8:746–752.
  • Mola-Abasi H, Shooshpasha I. Polynomial models controlling strength of zeolite-cement-sand mixtures. Sci Iran Trans A Civ Eng. 2017;24:526–536.
  • Consoli NC. A method proposed for the assessment of failure envelopes of cemented sandy soils. Eng Geol. 2014;169:61–68.
  • Consoli NC, Foppa D, Festugato L, et al. Key parameters for strength control of artificially cemented soils. J Geotech Geoenviron Eng. 2007;133:197–205.
  • Kohestani VR, Hassanlourad M. Modeling the mechanical behavior of carbonate sands using artificial neural networks and support vector machines. Int J Geomech. 2015;16:1–9.
  • Ellis GW, Yao C, Zhao R, et al. Stress-strain modeling of sands using artificial neural networks. J Geotech Eng. 1995;121:429–435.
  • Penumadu D, Zhao R. Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Comput Geotech. 1999;24:207–230.
  • Zhao H, Huang Z, Zou Z. Simulating the stress-strain relationship of geomaterials by support vector machine. Math Probl Eng. 2014;2014:Article ID 482672. http://dx.doi.org/10.1155/2014/482672.
  • Banimahd M, Yasrobi SS, Woodward PK. Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification. Comput Geotech. 2005;32:377–386.
  • Moayed RZ, Kordnaeij A, Mola-Abasi H. Compressibility indices of saturated clays by group method of data handling and genetic algorithms. Neural Comput Appl. 2017;28(1):551–564.
  • Ardalan H, Eslami A, Nariman-Zadeh N. Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Comput Geotech. 2009;36:616–625.
  • Kalantary F, Ardalan H, Nariman-Zadeh N. An investigation on the Su–N SPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol. 2009;104:144–155.
  • Ardakani A, Kordnaeij A. Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng. 2017;1–14.
  • Eslami A, Mola-Abasi H, Shourijeh PT. A polynomial model for predicting liquefaction potential from cone penetration test data. Sci Iran Trans A Civ Eng. 2014; 21:44–52.
  • Kordnaeij A, Kalantary F, Kordtabar B, et al. Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found. 2015;55:1335–1345.
  • Eslami A, Alimirzaei M, Aflaki E, et al. Deltaic soil behavior classification using CPTu records—Proposed approach and applied to fifty-four case histories. Mar Georesources Geotechnol. 2017;35:62–79.
  • Hassanlourad M, Ardakani A, Kordnaeij A, et al. Dry unit weight of compacted soils prediction using GMDH-type neural network. Eur Phys J Plus. 2017;132:357. https://doi.org/10.1140/epjp/i2017-11623-5.
  • Mola-Abasi H, Kordtabar B, Kordnaeij A. Effect of natural zeolite and cement additive on the strength of sand. Geotech Geol Eng. 2016;34:1539–1551.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.