TY - JOUR ID - 113899 TI - Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirical and Neural Network Models JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Kanchidurai, S. AU - Krishnan, P.A. AU - Baskar, K. AD - Assistant Professor, School of Civil Engineering, SASTRA Deemed to be University, Thanjavur, India AD - Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India Y1 - 2020 PY - 2020 VL - 4 IS - 4 SP - 24 EP - 35 KW - Masonry KW - PNM mesh KW - Compressive strength KW - analytical modelling KW - statistical KW - Artificial neural network (ANN) KW - Multiple linear regression predictive model (MLRPM) DO - 10.22115/scce.2020.228611.1213 N2 - 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. UR - https://www.jsoftcivil.com/article_113899.html L1 - https://www.jsoftcivil.com/article_113899_127f4fb40fcac9f83540a7dda95421c1.pdf ER -