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Computers and Concrete
  Volume 19, Number 6, June 2017 , pages 711-716

Predicting residual moment capacity of thermally insulated RC beams exposed to fire using artificial neural networks
Hakan Erdem

    This paper presents a method using artificial neural networks (ANNs) to predict the residual moment capacity of thermally insulated reinforced concrete (RC) beams exposed to fire. The use of heat resistant insulation material protects concrete beams against the harmful effects of fire. If it is desired to calculate the residual moment capacity of the beams in this state, the determination of the moment capacity of thermally insulated beams exposed to fire involves several consecutive calculations, which is significantly easier when ANNs are used. Beam width, beam effective depth, fire duration, concrete compressive and steel tensile strength, steel area, thermal conductivity of insulation material can influence behavior of RC beams exposed to high temperatures. In this study, a finite difference method was used to calculate the temperature distribution in a cross section of the beam, and temperature distribution, reduction mechanical properties of concrete and reinforcing steel and moment capacity were calculated using existing relations in literature. Data was generated for 336 beams with different beam width (bw), beam account height (h), fire duration (t), mechanical properties of concrete (fcd) and reinforcing steel (fyd), steel area (As), insulation material thermal conductivity (kinsulation). Five input parameters (bw, h, fcd, fyd, As and kinsulation) were used in the ANN to estimate the moment capacity (Mr). The trained model allowed the investigation of the effects on the moment capacity of the insulation material and the results indicated that the use of insulation materials with the smallest value of the thermal conductivities used in calculations is effective in protecting the RC beam against fire.
Key Words
    fire; thermally insulation material; thermal conductivity; residual moment capacity; reinforced concrete; beam; artificial neural networks
Hakan Erdem: Department of Civil Engineering, Nigde University, 51240, Nigde, Turkey

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