(Peer Review + Similarity + Document) The Prediction of Stiffness Reduction Non-Linear Phase in Bamboo Reinforced Concrete Beam Using the Finite Element Method (FEM) and Artificial Neural Networks (ANNs)

muhtar, muhtar (2020) (Peer Review + Similarity + Document) The Prediction of Stiffness Reduction Non-Linear Phase in Bamboo Reinforced Concrete Beam Using the Finite Element Method (FEM) and Artificial Neural Networks (ANNs). MDPI AG, Switzerland.

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Official URL: https://www.mdpi.com/1999-4907/11/12/1313

Abstract

: This paper discusses the reduction of the stiffness of bamboo reinforced concrete (BRC) beams to support the use of bamboo as an environmentally friendly building material. Calculation of cross-section stiffness in numerical analysis is very important, especially in the non-linear phase. After the initial crack occurs, the stiffness of the cross-section will decrease with increasing load and crack propagation. The calculation of the stiffness in the cross-section of the concrete beam in the non-linear phase is usually approximated by giving a reduction in stiffness. ACI 318-14 provides an alternative, reducing the stiffness of the plastic post-linear beam section through the moment of inertia (I) of the beam section for elastic analysis between 0.50Ig–0.25Ig. This study aims to predict the value of the reduction in the stiffness of the BRC beam section in the non-linear phase through the load-displacement relationship of experimental results validated by the Finite Element Method (FEM) and the Artificial Neural Networks (ANN) method. The experiment used 8 BRC beams and one steel-reinforced concrete (SRC) beam of singly reinforced with a size of 75 mm × 150 mm × 1100 mm. The beams were tested using a four-point loading method. The analysis results showed that the value of the stiffness reduction in the beam cross-sectional in the non-linear phase ranged from 0.5Ig–0.05Ig for BRC beams, and 0.75Ig–0.40Ig for SRC beams.

Item Type:	Peer Review
Uncontrolled Keywords:	stiffness reduction; bamboo reinforced concrete (BRC); finite element method (FEM); artificial neural networks (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 02:22
Last Modified:	11 Oct 2021 03:29
URI:	http://repository.unmuhjember.ac.id/id/eprint/8741

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