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Buildings 2018, 8(4), 62; https://doi.org/10.3390/buildings8040062

Deflection Prediction of No-Fines Lightweight Concrete Wall Using Neural Network Caused Dynamic Loads

1
Department of Civil Infrastructure Engineering, Faculty of Vocations, Institut Teknologi Sepuluh Nopember, Surabaya 60116, Indonesia
2
Department of Engineering Physic, Faculty of Industrial Technologi, Institut Teknologi Sepuluh Nopember, Surabaya 60116, Indonesia
*
Author to whom correspondence should be addressed.
Received: 19 March 2018 / Revised: 16 April 2018 / Accepted: 17 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Masonry Buildings: Research and Practice)
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Abstract

No-fines lightweight concrete wall with horizontal reinforcement refers to an alternative material for wall construction with an aim of improving the wall quality towards horizontal loads. This study is focused on artificial neural network (ANN) application to predicting the deflection deformation caused by dynamic loads. The ANN method is able to capture the complex interactions among input/output variables in a system without any knowledge of interaction nature and without any explicit assumption to model form. This paper explains the existing data research, data selection and process of ANN modelling training process and validation. The results of this research show that the deformation can be predicted more accurately, simply and quickly due to the alternating horizontal loads. View Full-Text
Keywords: wall; hysteresis; dynamic; no-fines lightweight concrete; artificial neural network wall; hysteresis; dynamic; no-fines lightweight concrete; artificial neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bayuaji, R.; Ruki Biyanto, T. Deflection Prediction of No-Fines Lightweight Concrete Wall Using Neural Network Caused Dynamic Loads. Buildings 2018, 8, 62.

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