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Assessment and ANN model development of natural light transmittance of light-transmitting concreteopen access

Authors
Chiew, Shing MeiIbrahim, Izni SyahrizalMohd Ariffin, Mohd AzreenLee, Han-SeungSingh, Jitendra Kumar
Issue Date
Dec-2023
Publisher
Elsevier B.V.
Keywords
Artificial neural network; Explicit equations; Light-transmitting concrete; Natural light transmittance
Citation
Results in Engineering, v.20, pp 1 - 14
Pages
14
Indexed
SCOPUS
ESCI
Journal Title
Results in Engineering
Volume
20
Start Page
1
End Page
14
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115503
DOI
10.1016/j.rineng.2023.101416
ISSN
2590-1230
Abstract
This study aims to reveal the potential of Light-transmitting concrete (LTC) in transmitting natural light or sunlight, and to investigate the relationship between fibre diameter, fibre spacing, solar incidence angle, surface area, and light transmittance properties. The artificial neural network model as well as explicit equations from the model were developed to predict the light transmittance of LTC. The surface area was altered by varying LTC block arrangements from one to six. It was found that light incidence angle significantly affected the light transmittance of LTC. The highest light transmittance of LTC was achieved near solar noon, but it decreased drastically as soon as the solar incidence angle exceeded the acceptance angle. The effect of the LTC surface area and solar incidence angle on light transmittance diminished with a smaller fibre diameter or larger fibre spacing. The ANN model and explicit equations developed from the network provide good accuracy in predicting light transmittance of LTC. © 2023 The Authors
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ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)
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