NEURAL NETWORK PREDICTION OF CONFINED PEAK STRESSES OF RC COLUMNS

Abstract

The research presents ANN ("Artificial Neural Networks") estimation of confined peak strength for R.C columns. The modeling of the strength of reinforced concrete columns by uses of the (FEM) finite element method gets many difficulties, starting in geometric representation down to nonlinearities due to loads. The use of neural networks trained well can give us a model that can be utilized as an alternative and successful model for those columns. Experimental sets of data for concrete of square and circular concrete columns were gathered from many researches to develop an Artificial Neural Network formula as input data set parameters consist of ultimate strengths, size of mainly longitudinal and ties reinforcements, compressive concrete strength, thickness of concrete cover for reinforcement, specimen geometric dimension, and stirrup bars spacing. Confined Peaking Compressive Strength (CPCS) of square and circular concrete columns is predicted by neural networks technique and sorted with analytical models and found that they are scientifically accepted. The prediction was performed by package program (Mat Lap).