Compressive strength estimation of mesh embedded masonry prism using empirical and neural network models

Abstract

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.