Erdem, Hakan2019-08-012019-08-0120150950-06181879-0526https://dx.doi.org/10.1016/j.conbuildmat.2015.10.049https://hdl.handle.net/11480/3833This 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. (C) 2015 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessReinforced concreteFireBeamMoment capacityArtificial neural networkArticle101303810.1016/j.conbuildmat.2015.10.0492-s2.0-84944691286Q1WOS:000366227100004Q1