A Predictive Model Based ANN for Compressive Strength Assessment of the Mortars Containing Metakaolin

Document Type : Regular Article

Authors

Department of Civil Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

Abstract

A predictive model based on the artificial neural network (ANN) was generated to assess the compressive strength of Mortar incorporating metakaolin (MK). For this purpose, a database was gathered from different works of literature for use in the ANN model. Therefore, five predictive variables as the inputs of the ANN model were considered, including the age of the samples, the ratio of MK replacement, the ratio of water to the binder, the ratio of superplasticizer, and the ratio of binder to sand. Using the constructed ANN model, a new formula has been presented, which can predict the compressive strength of the mortars incorporating MK. Then, the performance of the presented formulae was examined. The obtained conclusions indicated that the evaluated formula can predict the compressive strength of the mortars containing MK. Also, in the end, Garson’s algorithm as a sensitivity algorithm was employed to examine the effect of each predictive variable on the compressive strength of the mortars incorporating MK. The results reveal that the binder-sand ratio is a more important parameter in determining the compressive strength of the mortars incorporating MK.

Highlights

Google Scholar

Keywords

Main Subjects


[1]     Muduli R, Mukharjee BB. Effect of incorporation of metakaolin and recycled coarse aggregate on properties of concrete. J Clean Prod 2019;209:398–414. doi:10.1016/j.jclepro.2018.10.221.
[2]     Tafraoui A, Escadeillas G, Vidal T. Durability of the Ultra High Performances Concrete containing metakaolin. Constr Build Mater 2016;112:980–7. doi:10.1016/j.conbuildmat.2016.02.169.
[3]     Pouhet R, Cyr M. Formulation and performance of flash metakaolin geopolymer concretes. Constr Build Mater 2016;120:150–60. doi:10.1016/j.conbuildmat.2016.05.061.
[4]     Saboo N, Shivhare S, Kori KK, Chandrappa AK. Effect of fly ash and metakaolin on pervious concrete properties. Constr Build Mater 2019;223:322–8. doi:10.1016/j.conbuildmat.2019.06.185.
[5]     Sujjavanich S, Suwanvitaya P, Chaysuwan D, Heness G. Synergistic effect of metakaolin and fly ash on properties of concrete. Constr Build Mater 2017;155:830–7. doi:10.1016/j.conbuildmat.2017.08.072.
[6]     Vu D., Stroeven P, Bui V. Strength and durability aspects of calcined kaolin-blended Portland cement mortar and concrete. Cem Concr Compos 2001;23:471–8. doi:10.1016/S0958-9465(00)00091-3.
[7]     Parande AK, Ramesh Babu B, Aswin Karthik M, Deepak Kumaar KK, Palaniswamy N. Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar. Constr Build Mater 2008;22:127–34. doi:10.1016/j.conbuildmat.2006.10.003.
[8]     Courard L, Darimont A, Schouterden M, Ferauche F, Willem X, Degeimbre R. Durability of mortars modified with metakaolin. Cem Concr Res 2003;33:1473–9. doi:10.1016/S0008-8846(03)00090-5.
[9]     Y S, A M. Rahmatian M. and Moghbeli K. Shear Strength Assessment of Slender Reinforced Normal Concrete Beams using Artificial Neural Networks. J Concr Struct Mater 2020;4:173–90.
[10]    Tohidi S, Sharifi Y. Neural networks for inelastic distortional buckling capacity assessment of steel I-beams. Thin-Walled Struct 2015;94:359–71. doi:10.1016/j.tws.2015.04.023.
[11]    Sharifi Y, Moghbeli A, Hosseinpour M, Sharifi H. Neural networks for lateral torsional buckling strength assessment of cellular steel I-beams. Adv Struct Eng 2019;22:2192–202. doi:10.1177/1369433219836176.
[12]    Y S, N M, A M. Shear capacity assessment of reinforced concrete deep beams using artificial neural network. J Concr Struct Mater 2018;3:30–43.
[13]    Y S, A M. Stepwise Regression for shear capacity assessment of steel fiber reinforced concrete beams. J Rehabil Civ Eng 2019;7:95–108.
[14]    Y S, F L, A. M. Compressive strength prediction by ANN formulation approach for FRP confined rectangular concrete columns. J Rehabil Civ Eng 2019;7:182–203.
[15]    Sharifi Y, Moghbeli A, Hosseinpour M, Sharifi H. Study of Neural Network Models for the Ultimate Capacities of Cellular Steel Beams. Iran J Sci Technol Trans Civ Eng 2019. doi:10.1007/s40996-019-00281-z.
[16]    Tohidi S, Sharifi Y. Inelastic lateral-torsional buckling capacity of corroded web opening steel beams using artificial neural networks. IES J Part A Civ Struct Eng 2015;8:24–40. doi:10.1080/19373260.2014.955139.
[17]    Sharifi Y, Hosseinpour M, Moghbeli A, Sharifi H. Lateral Torsional Buckling Capacity Assessment of Castellated Steel Beams Using Artificial Neural Networks. Int J Steel Struct 2019;19:1408–20. doi:10.1007/s13296-019-00217-3.
[18]    Sharifi Y, Tohidi S. Ultimate capacity assessment of web plate beams with pitting corrosion subjected to patch loading by artificial neural networks. Adv Steel Constr 2014;10:325–50.
[19]    Tohidi S, Sharifi Y. Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network. Thin-Walled Struct 2016;100:48–61. doi:10.1016/j.tws.2015.12.007.
[20]    Sharifi Y, Tohidi S. Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks — elastic investigation. Front Struct Civ Eng 2014;8:167–77. doi:10.1007/s11709-014-0236-z.
[21]    Tohidi S, Sharifi Y. A new predictive model for restrained distortional buckling strength of half-through bridge girders using artificial neural network. KSCE J Civ Eng 2016;20:1392–403. doi:10.1007/s12205-015-0176-8.
[22]    Tohidi S, Sharifi Y. Empirical Modeling of Distortional Buckling Strength of Half-Through Bridge Girders via Stepwise Regression Method. Adv Struct Eng 2015;18:1383–97. doi:10.1260/1369-4332.18.9.1383.
[23]    Cascardi A, Micelli F, Aiello MA. An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns. Eng Struct 2017;140:199–208. doi:10.1016/j.engstruct.2017.02.047.
[24]    Barkhordari K, Entezari Zarch H. Prediction of permanent earthquake-induced deformation in earth dams and embankments using artificial neural networks. Civ Eng Infrastructures J 2015;48:271–83.
[25]    Rezazadeh Eidgahee D, Rafiean AH, Haddad A. A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iran J Sci Technol Trans Civ Eng 2020;44:219–29. doi:10.1007/s40996-019-00263-1.
[26]    Hosseinpour M, Sharifi H, Sharifi Y. Stepwise regression modeling for compressive strength assessment of mortar containing metakaolin. Int J Model Simul 2018:1–9. doi:10.1080/02286203.2017.1422096.
[27]    Sharifi Y, Hosseinpour M. Adaptive neuro-fuzzy inference system and stepwise regression for compressive strength assessment of concrete containing metakaolin. Iran Univ Sci Technol 2019;9:251–72.
[28]    Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signals, Syst 1989;2:303–14. doi:10.1007/BF02551274.
[29]    Marquardt DW. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J Soc Ind Appl Math 1963;11:431–41. doi:10.1137/0111030.
[30]    Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 1994;5:989–93. doi:10.1109/72.329697.
[31]    Hristev R. The ANN book. GNU public license. 1998.
[32]    Frank IE, Todeschini R. The data analysis handbook. Elsevier; 1994.
[33]    Gandomi AH, Mohammadzadeh S. D, Pérez-Ordóñez JL, Alavi AH. Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups. Appl Soft Comput 2014;19:112–20. doi:10.1016/j.asoc.2014.02.007.
[34]    Smith GN. Probability and statistics in civil engineering. Collins Prof Tech Books 1986;244.
[35]    Garson DG. Interpreting neural network connection weights 1991.
[36]    Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM. A hybrid computational approach to derive new ground-motion prediction equations. Eng Appl Artif Intell 2011;24:717–32. doi:10.1016/j.engappai.2011.01.005.