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

Munzir Absa; Tulus Setiawan

doi:10.58526/jsret.v2i1.66

Authors

Munzir Absa Universitas Malikussaleh
Tulus Setiawan

DOI:

https://doi.org/10.58526/jsret.v2i1.66

Keywords:

Weight Optimization Algorithm, Neural Network, Lightweight Brick, Mechanical Properties

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.

Downloads

Download data is not yet available.

References

Gunawan, T. S., Ashraf, A., Riza, B. S., Haryanto, E. V., Rosnelly, R., Kartiwi, M., & Janin, Z. (2020). Development of video-based emotion recognition using deep learning with Google Colab. Telkomnika (Telecommunication Computing Electronics and Control), 18(5), 2463–2471. https://doi.org/10.12928/TELKOMNIKA.v18i5.16717

Harsiti, Muttaqin, Z., & Srihartini, E. (2022). Penerapan Metode Regresi Linier Sederhana Untuk Prediksi Persediaan Obat Jenis Tablet. JSiI (Jurnal Sistem Informasi), 9(1), 12–16. https://doi.org/10.30656/jsii.v9i1.4426

Kalpana, M., & Mohith, S. (2020). Study on autoclaved aerated concrete: Review. Materials Today: Proceedings, 22(xxxx), 894–896. https://doi.org/10.1016/j.matpr.2019.11.099

Kifli, Z., Absa, M., & Musyafa, A. (2017). Prediction of Mechanical Properties of Light Weight Brick Composition Using Artificial Neural Network on Autoclaved Aerated Concrete. Asian Journal of Applied Sciences, 05(03), 556–565.

Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., & Holt, J. (2017). Large-scale distributed L-BFGS. Journal of Big Data, 4(1). https://doi.org/10.1186/s40537-017-0084-5

Ramchoun, H., Amine, M., Idrissi, J., Ghanou, Y., & Ettaouil, M. (2016). Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 26. https://doi.org/10.9781/ijimai.2016.415

Suryani, N., & Munasir. (2015). Fabrikasi Bata Ringan Tipe Celluler Lightweight Concrete dengan Bahan Dasar Pasir Vulkanik Gunung Kelud Sebagai Pengganti Fly Ash. Jurnal Inovasi Fisika Indonesia, 04(03), 106–111. 2302-4216