Assessment of Seismic Behavior of Mid-Rise R/C Slab Column Buildings in Cyprus Using Fragility Curves and Artificial Neural Network

One of the structural systems in Cyprus is slab-column frame buildings with wide beams and rectangular columns. In this study 4-, 6- and 8-story buildings with regular plan of mid-rise wide-beam buildings in Famagusta, Cyprus were defined. Fragility curves were employed as one of the important seismic assessment tools and constructed using incremental dynamic analysis (IDA) method. In this study, a set of earthquake records were chosen to represent the soil properties and strike-slip type of faulting in this region which also have a good correlation with Turkish design spectrum. These records were scaled to ten different levels of peak ground acceleration (PGA) from PGA=0.1 to PGA=1.0g. The Park & Ang damage index and log-normal cumulative distribution function were used as proper damage index and probability function, respectively. Based on IDA curves, two damage levels including; immediate occupancy (IO) and collapse prevention (CP) were obtained for this type of building and they were compared with criteria which are suggested by FEMA 356. Also, the effects of P-delta and aftershock were evaluated. Since the nonlinear time history analysis is time consuming, requires complex calculations and powerful computers, for rapid evaluation of damage the artificial neural network (ANN) was used as an efficient tool. In this study, using the results of numerical simulations, 600 data were generated and applied to a multi-layer perceptron (MLP) neural network in order to predict the imposed damage of these sample buildings. In training process, ten different activation functions were examined to find the best kernel function. Also the optimum hidden layer neurons were calculated by using minimum test error method. In this network, 70 %, 15 % and 15 % of all data were used for training, validating and testing process, respectively. Based on obtained results from ANN, the fragility curves were drawn and compared with the obtained curves from IDA. This application of network also is able to predict the top displacement and the base shear force of sample buildings. Another application of ANN was used for classification of global imposed damage based on Park & Ang investigation. For this aim, two networks include; multi-class support vector machine (M-SVM) and combination of MLP neural network with M-SVM (MM-SVM) were applied and the label of each actual class was compared with predicted class. The results showed that the ANNs are able to predict and classify the damages with high accuracy and also they can be used as an appropriate and reliable alternative tool for rapid seismic evaluation of structural systems. Finally, an existing model of R/C wide-beam building (test model) was considered and the obtained fragility curves from classical method and ANN were compared and discussed. Keywords: aftershock effect, artificial neural network, damage classification, damage prediction, fragility curve, incremental dynamic analysis, Park & Ang damage index, R/C wide-beam buildings, seismic behavior.