Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks

In this study, the application of artificial neural networks (ANN) to predict the ultimate moment capacity of reinforced concrete (RC) slabs in fire is investigated. An ANN model is built, trained and tested using 294 data for slabs exposed to fire. The data used in the ANN model consists of seven input parameters, which are the distance from the extreme fiber in tension to the centroid of the steel on the tension side of the slab (d'), the effective depth (d), the ratio of previous parameters (d'/d), the area of reinforcement on the tension face of the slab (A"s), the fire exposure time (t), the compressive strength of the concrete (f"c"d), and the yield strength of the reinforcement (f"y"d). It is shown that ANN model predicts the ultimate moment capacity (M"u) of RC slabs in fire with high degree of accuracy within the range of input parameters considered. The moment capacities predicted by ANN are in line with the results provided by the ultimate moment capacity equation. These results are important as ANN model alleviates the problem of computational complexity in determining M"u.

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