TY  - JOUR
T1  - Predicting the Long Term Deflection of Flexural Members
Using Artificial Neural Networks
AU - K. Zaki, Rana I. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 23
SP  - 10039
EP  - 10045
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.10039.10045
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.10039.10045
KW  - Long term deflection
KW  -concrete
KW  -artificial neural networks
KW  -flexural members
KW  -increment
KW  -beams
AB  - A long term deflection response of reinforced concrete flexural members is influenced by many factors
like compression reinforcement, creep coefficient, shrinkage strain, total time of experiment (years) and the
ultimate compressive strength. A statistical approach artificial neural network for the predicting of long term
deflection of reinforced concrete beams or slabs is proposed in this study. The artificial neural network
predicted approach from this study was compared with (ACI-318) code equation. Results of artificial neural
network was discussed and compared with the experimental data obtained from conducted studies. It showed
a good agreement. However, the predicted approach was found to be too simplified to assess the increment of
the long-term deflection.
ER  -