(Peer review & Similarity) The Prediction of Sti�ness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs)

muhtar, muhtar and Gunasti, Amri and Suhardi, Suhardi and Nursaid, Nursaid and Irawati, Irawati and Dewi, Ilanka Cahya and Dasuki, Moh and Ariyani, Sofia and Fitriana, Fitriana and Mahmudi, Idris and Abadi, Taufan and Rahman, Miftahur and Hidayatullah, Syarif and Nilogiri, Agung and Galuh, Senki Desta and Wardoyo, Ari Eko and Hamduwibawa, Rofi Budi (2020) (Peer review & Similarity) The Prediction of Sti�ness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs). Crystals.

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Abstract

Stiffness is the main parameter of the beam’s resistance to deformation. Based on advanced research, the sti�ness of bamboo-reinforced concrete beams (BRC) tends to be lower than the sti�ness 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 sti�ness 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 75mm� 150mm� 1100 mm. The testing method uses the four-point method with simple support. The results of the analysis showed the similarity between the sti�ness 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
Divisions: Faculty of Engineering > Department of Electronics Engineering (S1 - Undergraduate Thesis)
Depositing User: Fitriana Fitriana
Date Deposited: 02 Mar 2021 01:33
Last Modified: 17 Mar 2021 05:17
URI: http://repository.unmuhjember.ac.id/id/eprint/8649

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