@article{MAKHILLJEAS2018132317225,
    title = {Predicting the Long Term Deflection of Flexural Members
Using Artificial Neural Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {23},
    pages = {10039-10045},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.10039.10045},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.10039.10045},
    author = {Rana I.},
    keywords = {Long term deflection,concrete,artificial neural networks,flexural members,increment,beams},
    abstract = {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.}
    }