(Peer Review + CA) The Prediction of Stiffness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs)

muhtar, muhtar (2020) (Peer Review + CA) The Prediction of Stiffness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs). MDPI AG, Switzerland.

	Text Peer review + Email.pdf Download (18MB)
	Text Turnitin.pdf Download (2MB)
	Text The Prediction of Stiffness of Bamboo-Reinforced Concrete Beams Using Experiment.pdf Download (18MB)

Official URL: https://www.mdpi.com/2073-4352/10/9/757

Abstract

Stiffness is the main parameter of the beam’s resistance to deformation. Based on advanced research, the stiffness of bamboo-reinforced concrete beams (BRC) tends to be lower than the stiffness of steel-reinforced concrete beams (SRC). However, the advantage of bamboo-reinforced concrete beams has enough good ductility according to the fundamental properties of bamboo, which have high tensile strength and high elastic properties. This study aims to predict and validate the stiffness of bamboo-reinforced concrete beams from the experimental results data using artificial neural networks (ANNs). The number of beam test specimens were 25 pieces with a size of 75 mm × 150 mm × 1100 mm. The testing method uses the four-point method with simple support. The results of the analysis showed the similarity between the stiffness of the beam’s experimental results with the artificial neural network (ANN) analysis results. The similarity rate of the two analyses is around 99% and the percentage of errors is not more than 1%, both for bamboo-reinforced concrete beams (BRC) and steel-reinforced concrete beams (SRC).

Item Type:	Peer Review
Uncontrolled Keywords:	bamboo-reinforced concrete (BRC); stiffness prediction; artificial neural network (ANN)
Subjects:	600 Technology and Applied Science > 620 Engineering > 624 Civil Engineering
Divisions:	Faculty of Engineering > Department of Civil Engineering (S1)
Depositing User:	Muhtar Muhtar
Date Deposited:	06 Mar 2021 02:21
Last Modified:	11 Oct 2021 03:31
URI:	http://repository.unmuhjember.ac.id/id/eprint/8739

Actions (login required)

View Item