State of the Art of Deep Learning Method to Predict the Compressive Strength of Concrete

Setiawan, Agustinus Agus and Soegiarso, Roesdiman and Hardjasaputra, Harianto and Lina, Lina (2021) State of the Art of Deep Learning Method to Predict the Compressive Strength of Concrete. Technology Reports of Kansai University, 63 (06). pp. 7727-7737. ISSN 04532198

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Abstract

In recent years research in the field of Artificial Intelligence (AI) has shown a fairly rapid and extensive development. The application of AI in the form of artificial neural networks in the field of Civil Engineering has also shown significant developments. In line with scientific advances in the field of numerical computing, artificial intelligence, also known as AI, has gained popularity in recent years (AI). One of the branches of AI is Machine Learning (ML). Machine Learning is a smart system that can learn and predict output based on learning done on the system. One form of ML is an artificial neural network (artificial neural network, ANN). ANN is a numerical computation model created by mimicking the workings of the human brain. ANN then developed extensively, with a greater number of neurons or by increasing the number of hidden layers. This development leads to a learning model known as Deep Learning (DL). This paper has an objective to describes how deep learning method, one of AI branch, can be used to predict the compressive strength of concrete, especially geopolymer concrete.

Item Type:	Artikel
Uncontrolled Keywords:	artificial intelligence, machine learning, deep learning, geopolymer concrete.
Subjects:	A General Works > AC Collections. Series. Collected works
Divisions:	Universitas Pembangunan Jaya
Depositing User:	Alexandro Andika
Date Deposited:	11 May 2023 02:01
Last Modified:	11 May 2023 02:01
URI:	http://eprints.upj.ac.id/id/eprint/5114

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