Artificial Neural Networks (ANN) are information – processing systems whose architecture mimic the biological system of the brain. Recently, civil engineers have utilized ANN for various applications especially in the modeling of civil engineering systems.
In my case, I developed an ANN model for predicting the confined compressive strength and strain of a circular reinforced concrete column. The model has seven input nodes: (1) unconfined concrete cylinder strength, f’c; (2) concrete core diameter, d, where the core is the part of the section enclosed by the centroidal axis of the hoop or ties; (3) column height, H; (4) yield strength of lateral reinforcement, fyh; (5) volumetric ratio of lateral reinforcement; (6) tie spacing, s ; and (7) vertical steel or longitudinal reinforcement ratio. The two output nodes, on the other hand, represent the peak stress or compressive strength of confined concrete circular column, f’cc and the strain, ecc , at peak stress. Shown below is the GUI of the Visual Basic program of the ANN model.
The predictions of the compressive strength or peak stress, f’cc, of confined concrete columns and the corresponding strain, ecc, have been a subject of various researches, both analytical and experimental. The values of these two parameters are usually used in the analytical models developed for describing the stress-strain relationship for confined concrete.
You may run the ANN model at http://mysite.dlsu.edu.ph/faculty/oretaa. Go to the Software section. Papers on the model can also be downloaded from this site.