Comparison of Different Weight Optimization Algorithm in Neural Network to Predict Mechanical Properties of AAC Lightweight Brick

Abstract

This research aims to find the optimal weight optimization algorithm and number of hidden nodes that can be used in Artificial Neural Network to predict mechanical properties (density and compressive strength) of Autoclaved Aerated Concrete (AAC) lightweight brick. The dataset is obtained from secondary source, with a total of 51 data points. From this dataset, the relationship between constituent elements of AAC with its density and compressive strength is modeled using ANN. It was found that the best weight optimization algorithm that can be used for this dataset is the LBFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) algorithm. The optimum hidden layer node is found to be 90 nodes. With this parameters, the ANN can predict density and compressive strength of AAC lightweight brick with accuracy of 93.51% and margin of error around 6.49%. The accuracy of the prediction can be improved by appending the dataset with data points from secondary sources or by doing more experiments and tests.