What is it about?
This research mainly focuses on developing a multi-layer back propagation artificial neural network (ANN) model to predict the load capacity of simply supported deep beams reinforced with CFRP. Thorough experimental results have been compiled from several previous studies to build up a database that can be used to train and test the developed ANN model. Different input neurons were adopted including the shear span to depth ratio (a/d), the effective concrete compressive strength of concrete (f_c^'), the cross-sectional area of diagonal strut (Astrut) and the sin angle of the inclined strut (sinɸ). On the other hand, the ANN output was the ultimate load of simply supported deep beams. Parametric research is also carried out to determine the extent to which certain parameters employed in the created neural network model have an impact on the load capacity. The results showed that ANN can reasonably estimate the load capacity of simply supported CFRP deep beams reinforced with CFRP.
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Why is it important?
our findings show that, the load capacity of simply support CFRP deep beams can be accurately predicted by the proposed ANN model. Moreover, ANN model helps to reduce the time consuming and effort and increase the accuracy of predictive models based on data gathered over time from previous study.
Perspectives
this article lead to to concern the models as a replace to experimental and give more opportunity to use ANN to predict different properties in civil engineering in specific and other subjects in general
Munir M Mahgub Altamami
Read the Original
This page is a summary of: Load capacity prediction of simply supported CFRP deep beams using artificial neural networks, January 2024, American Institute of Physics,
DOI: 10.1063/5.0215164.
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