Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirical and Neural Network Models

Document Type : Regular Article


1 Assistant Professor, School of Civil Engineering, SASTRA Deemed to be University, Thanjavur, India

2 Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India


Presently, the mesh embedment in masonry is becoming a trendy research topic. In this paper, the mesh embedded masonry prism was cast and tested. The experimental data were used for the analytical modelling. Compressive strength (CS) test was conducted for forty five masonry prism specimens with and without poultry netting mesh (PNM) embedment in the bed joints. The small mesh embedment in the masonry prism provides the better strength improvement as well as the endurance. The size of masonry prism was 225×105×176 mm. Uniformity was maintained in all prisms as per the guidelines given in ASTM C1314. Compressive strength experimental results are compared with a new proposed regression equation. The equation needs nine input parameters and two adjustment coefficients. The masonry mortar strength and mesh embedment are considered as input parameter. The experimental results were predicted by proposed Artificial Neural Network model. The validated results were gives better and more accuracy compared to the statistical and MLRPM models.


Google Scholar


Main Subjects

[1]     Cascardi A, Micelli F, Aiello MA. Analytical model based on artificial neural network for masonry shear walls strengthened with FRM systems. Compos Part B Eng 2016;95:252–63. doi:10.1016/j.compositesb.2016.03.066.
[2]     Lan G, Wang Y, Zeng G, Zhang J. Compressive strength of earth block masonry: Estimation based on neural networks and adaptive network-based fuzzy inference system. Compos Struct 2020;235:111731. doi:10.1016/j.compstruct.2019.111731.
[3]     Garzón-Roca J, Marco CO, Adam JM. Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Eng Struct 2013;48:21–7. doi:10.1016/j.engstruct.2012.09.029.
[4]     Zhou Q, Wang F, Zhu F. Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems. Constr Build Mater 2016;125:417–26. doi:10.1016/j.conbuildmat.2016.08.064.
[5]     Garzón-Roca J, Adam JM, Sandoval C, Roca P. Estimation of the axial behaviour of masonry walls based on Artificial Neural Networks. Comput Struct 2013;125:145–52. doi:10.1016/j.compstruc.2013.05.006.
[6]     Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. doi:10.1016/j.jestch.2019.05.013.
[7]     Kalman Sipos T, Parsa P. Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks. J Soft Comput Civ Eng 2020;4:111–26. doi:10.22115/scce.2020.221268.1181.
[8]     Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X. Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 2018;175:562–9. doi:
[9]     Czarnecki S. Non-destructive Evaluation of the Bond between a Concrete Added Repair Layer with Variable Thickness and a Substrate Layer Using ANN. Procedia Eng 2017;172:194–201. doi:10.1016/j.proeng.2017.02.049.
[10]    Xu J, Zhao X, Yu Y, Xie T, Yang G, Xue J. Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks. Constr Build Mater 2019;211:479–91. doi:10.1016/j.conbuildmat.2019.03.234.
[11]    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.
[12]    Khosravikia F, Kurkowski J, Clayton P. Fragility of masonry veneers to human-induced Central U.S. earthquakes using neural network models. J Build Eng 2020;28:101100. doi:10.1016/j.jobe.2019.101100.
[13]    Lee J, Sanmugarasa K, Blumenstein M, Loo YC. Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM). Autom Constr 2008;17:758–72. doi:10.1016/j.autcon.2008.02.008.
[14]    Plevris V, Asteris PG. Modeling of masonry failure surface under biaxial compressive stress using Neural Networks. Constr Build Mater 2014;55:447–61. doi:10.1016/j.conbuildmat.2014.01.041.
[15]    Zhou Q, Zhu F, Yang X, Wang F, Chi B, Zhang Z. Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models. Constr Build Mater 2017;153:937–47. doi:10.1016/j.conbuildmat.2017.07.171.
[16]    Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Constr Build Mater 2010;24:709–18. doi:10.1016/j.conbuildmat.2009.10.037.
[17]    Mansouri I, Kisi O. Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos Part B Eng 2015;70:247–55. doi:10.1016/j.compositesb.2014.11.023.
[18]    Eskandari H, Nik MG, Eidi MM. Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN. Procedia Eng 2016;150:2185–92. doi:10.1016/j.proeng.2016.07.262.
[19]    American Society for Testing and Materials (ASTM). Standard Test Method for Compressive Strength of Masonry Prisms. ASTM C1314 – 16 2018.
[20]    Dayaratnam P. Brick and Reinforced Brick Structures. M. Primlani; 1987.