failure prediction of reinforced concrete tall building using artificial neural network

Reinforced concrete tall building failure, in residual areas, can cause catastrophic disaster if they can’t withstand during the destructive earthquakes. hence determining the damage of these buildings in earthquake and detecting the probable mechanism formation are necessary for insurance purposes in the urban areas. this paper aims to determine the failure modes of the flexural reinforced concrete buildings according to the damage of the beam and column. to achieve this goal, a 15-storey flexural reinforced concrete frame is modeled via idarc software, and nonlinear dynamic time history analysis is performed through 60 seismic accelerograms. then the collapse and non-collapse vectors are constructed obtaining the results of dynamic analysis in both modes. artificial neural network is used for the classification of the obtained modes. the results show good agreement in failures classes. hence make it possible to introduce the simple weight factor for frame status identification.