Predicting cement brick performance using Multilayer Perceptron Neural Networks

Abstract

Employing recycled solid waste as alternative construction materials presents considerable opportunities for diminishing waste disposal expenses and preserving natural resources. This study investigates the partial substitution of cement with Egg Shell Powder (ESP), Sea Shell Powder (SSP), and Recycled Mortar Powder (RMP) in cement mortar bricks. The study involved making cement mortar with a 1:2 ratio and testing mixtures with ESP, SSP, and RMP amounts of 5%, 10%, and 15%, respectively. The results show bricks with 10% ESP have better properties: they absorb 1.4% more water, are sound enough, have hardness of 21, and have compressive strength of 29.68 N/mm² after 28 d, and which is closely matched with predicted value 30.12 N/mm² from model. It is better at absorbing water by 7.53% when 5% SSP is added. It sound, has a hardness of 31, and a compressive strength of 28.63 N/mm², and predicted value of 28.56 N/mm². It absorbs 3.61% water, is sound enough, has a hardness of 23.75, and has a compressive strength of 29.62 N/mm² and predicted as 30 N/mm² when 15% RMP is used. A Multilayer Perceptron (MLP) Neural Network was utilized to evaluate brick performance, with predicted values aligning closely with the observed data. Results indicate the application of 10% ESP, 5% SSP, and 15% RMP enhances the characteristics of cement mortar bricks, fostering cost-effectiveness and sustainability in buildings.

Graphical abstract

Keywords

MLP Neural Network / cement mortar bricks / ESP / SSP / RMP

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