Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Abstract

When the only information available is the issue parameters, and the intended outwards, machine learning techniques like ANN (Artificial Neural Networks) and ANFIS (Adaptive neuro-fuzzy inference system) been proven to address the complex problems without duplicating the phenomena under investigation. The main prompting characteristics are the height-to-the-thicknesses ratio of prisms and the strength under compression of prisms and mortar were analyzed. As inputs, the prototypes are used as blocks and mortars. Both prototypes were accomplished and evaluated. Thirty-six data sets were gathered for testing in addition to verified technical and subsequently comparison with other empirical computation methods served to validate. The outwards show that the suggested prototypes have good forecast capabilities with negligible error rates. To assess and compare the structural behavior of structural completion of AAC block with the other types. At last, both the machine learning tools are good application and dependability.