Prediction of Zeolite-Cemented Sand Tensile Strength by GMDH type Neural Network

References

• Davies J, Fendukly LM. Behaviour of Soil-Cement Specimens in Unconfined Dynamic Compression. J. Mater. Sci. 1994;29(12):3255–3260.
   Web of Science ®Google Scholar
• Consoli NC, Cruz RC, Floss MF, Festugato L. Parameters Controlling Tensile and Compressive Strength of Artificially Cemented Sand. J. Geotech. Geoenvironmental Eng. 2009;136(5):759–763.
   Web of Science ®Google Scholar
• Fang YS, Chung YT, Yu FJ, Chen TJ. Properties of Soil-Cement Stabilised with Deep Mixing Method. Proc. Inst. Civ. Eng. Improv. 2001;5(2):69–74.
   Google Scholar
• Farouk A, Shahien MM. Ground Improvement Using Soil–cement Columns: Experimental Investigation. Alexandria Eng. J. 2013;52(4):733–740.
   Google Scholar
• Consoli NC, Rosa DA, Cruz RC, Dalla Rosa A. Water Content, Porosity and Cement Content as Parameters Controlling Strength of Artificially Cemented Silty Soil. Eng. Geol. 2011;122(3):328–333.
   Web of Science ®Google Scholar
• Rios S, Viana da Fonseca A, Baudet BA. Effect of the Porosity/cement Ratio on the Compression of Cemented Soil. J. Geotech. Geoenvironmental Eng. 2012;138(11):1422–1426.
   Web of Science ®Google Scholar
• Consoli NC, Foppa D. Porosity/cement Ratio Controlling Initial Bulk Modulus and Incremental Yield Stress of an Artificially Cemented Soil Cured under Stress. Géotech Lett 2014;4:22–26.
   Web of Science ®Google Scholar
• da Fonseca AV, Cruz RC, Consoli NC. Strength Properties of Sandy Soil–cement Admixtures. Geotech. Geol. Eng. 2009;27(6):681–686.
   Google Scholar
• Consoli NC, Rosa FD, Fonini A. Plate Load Tests on Cemented Soil Layers Overlaying Weaker Soil. J. Geotech. Geoenvironmental Eng. 2009;135(12):1846–1856.
   Web of Science ®Google Scholar
• Consoli NC, Vendruscolo MA, Prietto PDM. Behavior of Plate Load Tests on Soil Layers Improved with Cement and Fiber. J. Geotech. Geoenvironmental Eng. 2003;129(1):96–101.
   Web of Science ®Google Scholar
• Moore RK, Kennedy TW, Hudson WR. Factors Affecting the Tensile Strength of Cement-Treated Materials. Highw. Res. Rec. 1970;315:64–80.
   Google Scholar
• Poon CS, Lam L, Kou SC, Lin ZS. A Study on the Hydration Rate of Natural Zeolite Blended Cement Pastes. Constr. Build. Mater. 1999;13(8):427–432.
   Web of Science ®Google Scholar
• Canpolat F, Yılmaz K, Köse MM, Sümer M, Yurdusev MA. Use of Zeolite, Coal Bottom Ash and Fly Ash as Replacement Materials in Cement Production. Cem. Concr. Res. 2004;34(5):731–735.
   Web of Science ®Google Scholar
• Yılmaz B, Uçar A, Öteyaka B, Uz V. Properties of Zeolitic Tuff (Clinoptilolite) Blended Portland Cement. Build. Environ. 2007;42(11):3808–3815.
   Web of Science ®Google Scholar
• Mola-Abasi H, Shooshpasha I. Influence of Zeolite and Cement Additions on Mechanical Behavior of Sandy Soil. J. Rock Mech. Geotech. Eng. 2016;8(5):746–752.
   Web of Science ®Google Scholar
• MolaAbasi H, Shooshpasha I. Prediction of Zeolite-Cement-Sand Unconfined Compressive Strength Using Polynomial Neural Network. Eur. Phys. J. Plus 2016;131(4):1–12.
   Web of Science ®Google Scholar
• ASTM D. 2166 (1985) Standard Test Method for for Unconfined Compressive Strength of Cohessive Soil. In:. Am. Soc. Test. Mater., vol. 100. 2000.
   Google Scholar
• Kalantary F, Kordnaeij A. Prediction of Compression Index Using Artificial Neural Network. Sci. Res. Essays 2012;7(31):2835–2848.
   Google Scholar
• Erzin Y, Turkoz D. Use of Neural Networks for the Prediction of the CBR Value of Some Aegean Sands. Neural Comput. Appl. 2016;27(5):1415–1426.
   Web of Science ®Google Scholar
• Gordan B, Armaghani DJ, Hajihassani M, Monjezi M. Prediction of Seismic Slope Stability through Combination of Particle Swarm Optimization and Neural Network. Eng. Comput. 2016;32(1):85–97.
   Web of Science ®Google Scholar
• Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN. Evaluating the Modulus of Elasticity of Soil Using Soft Computing System. Eng. Comput. 2017;33(3):497–507.
   Web of Science ®Google Scholar
• Yadollahi MM, Benli A, Demirboga R. Application of Adaptive Neuro-Fuzzy Technique and Regression Models to Predict the Compressive Strength of Geopolymer Composites. Neural Comput. Appl. 2017;28(6):1453–1461.
   Web of Science ®Google Scholar
• Sharma LK, Vishal V, Singh TN. Predicting CO 2 Permeability of Bituminous Coal Using Statistical and Adaptive Neuro-Fuzzy Analysis. J. Nat. Gas Sci. Eng. 2017;42:216–225.
   Web of Science ®Google Scholar
• Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN. Prediction of Geomechanical Parameters Using Soft Computing and Multiple Regression Approach. Measurement 2017;99:108–119.
   Web of Science ®Google Scholar
• Sharma LK, Vishal V, Singh TN. Developing Novel Models Using Neural Networks and Fuzzy Systems for the Prediction of Strength of Rocks from Key Geomechanical Properties. Measurement 2017;102:158–169.
   Web of Science ®Google Scholar
• Ikizler SB, Vekli M, Dogan E, Aytekin M, Kocabas F. Prediction of Swelling Pressures of Expansive Soils Using Soft Computing Methods. Neural Comput. Appl. 2014;24(2):473–485.
   Web of Science ®Google Scholar
• Shahin MA. Load–settlement Modeling of Axially Loaded Steel Driven Piles Using CPT-Based Recurrent Neural Networks. Soils Found. 2014;54(3):515–522.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Ivakhnenko AG. Polynomial Theory of Complex Systems. IEEE Trans. Syst. Man. Cybern. 1971;1(4):364–378.
   Web of Science ®Google Scholar
• Farlow SJ. Self-Organizing Method in Modeling: GMDH Type Algorithm, 1984. n.d.
   Google Scholar
• Hassanlourad M, Ardakani A, Kordnaeij A, Mola-Abasi H. Dry Unit Weight of Compacted Soils Prediction Using GMDH-Type Neural Network. Eur. Phys. J. Plus 2017;132(8):357.
   Web of Science ®Google Scholar
• Moayed RZ, Kordnaeij A, Mola-Abasi H. Pressuremeter Modulus and Limit Pressure of Clayey Soils Using GMDH-Type Neural Network and Genetic Algorithms. Geotech. Geol. Eng. n.d.:36(1):165–178.
   Web of Science ®Google Scholar
• Naeini SA, Moayed RZ, Kordnaeij A, Mola-Abasi H. Elasticity Modulus of Clayey Deposits Estimation Using Group Method of Data Handling Type Neural Network. Measurement 2018;121:335–343.
   Web of Science ®Google Scholar
• Mola-Abasi H, Eslami A, Shourijeh PT. Shear Wave Velocity by Polynomial Neural Networks and Genetic Algorithms Based on Geotechnical Soil Properties. Arab. J. Sci. Eng. 2013;38(4):829–838.
   Web of Science ®Google Scholar
• Ardakani A, Kordnaeij A. Soil Compaction Parameters Prediction Using GMDH-Type Neural Network and Genetic Algorithm. Eur. J. Environ. Civ. Eng. 2017:1–14.
   Web of Science ®Google Scholar
• 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(1):44.
   Web of Science ®Google Scholar
• Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H. Prediction of Recompression Index Using GMDH-Type Neural Network Based on Geotechnical Soil Properties. Soils Found. 2015;55(6):1335–1345.
   Web of Science ®Google Scholar
• Moayed RZ, Kordnaeij A, Mola-Abasi H. Compressibility Indices of Saturated Clays by Group Method of Data Handling and Genetic Algorithms. Neural Comput. Appl. 2017:1–14.
   Web of Science ®Google Scholar
• Ladd RS. Preparing Test Specimens Using Undercompaction. 1978.
   Google Scholar
• Standard A. D3967. Standard Test Method for Splitting Tensile Strength of Intact Rock Core Specimens. Annu. B. ASTM Stand. Am. Soc. Test. Mater. West Conshohocken, PA 1995.
   Google Scholar