Predicting the moment capacity of RC slabs with insulation materials exposed to fire by ANN

Slabs prevent harmful effects of fire that may occur in any floor. However, it is necessary to protect the slabs from fire. Insulation materials may be appropriate to protect reinforced concrete (RC) slab from elevated temperature. In the present study, a model has been developed in artificial neural network (ANN) to predict the moment capacity ($M_r$) of RC slabs exposed to fire with insulation material. 672 data were obtained for ANN model through author's prepared program. Input layer in model consisted of seven input parameters; such as effective depth (d), ratio of d'/d, thermal conductivity coefficient ($k_{insulation}$), insulation materials thickness ($L_{insulation}$), reinforcement area ($A_{st}$), fire exposure time ($t_{\exp}$), and concrete compressive strength ($f_c$). The predicted $M_r$ by ANN was consistent with the obtained $M_r$ by author. It is proposed to ease computational complexity in determining $M_r$ using ANN. The effects of using insulation material on the moment capacity in RC slabs were also investigated. Insulating material with low thermal conductivity has been found to be more effective for durability to high temperature.