Predicting the moment capacity of RC beams exposed to fire using ANNs

Abstract This research investigates the implementation of artificial neural networks (ANNs) to estimate the moment capacity ( M r ) of reinforced concrete (RC) beams under rising temperatures due to fire. 280 data were obtained for ANN model. Input layer in ANN model consisted of eight input parameters; the beam width ( b w ), the beam depth ( d ), the ratio of ( b w / d ), distance from the beam edge to the center of the rebar ( d ′), the ratio of ( d ′/ d ), fire time ( t exposure ), the reinforcement area ( A st ), and concrete compressive strength ( f c ). It is shown that the ANN model can be used to predict the M r of RC beams exposed to fire with high accuracy. The predicted M r by ANN are consistent with the results obtained using M r equation. It was observed from the results the ANN model reduces the computational complexity problem in determining M r . Consequently, the ANN model was used to examine the effects of the inputs parameters on M r .