Investigation of the ANNs' potential for reliable assessment of r/c frame's seismic damage using different performance evaluation metrics

Abstract

The development of a reliable method for the rapid assessment of the expected level of seismic damage of buildings constructed in countries with high seismicity areas is one of the crucial issues of current research, so that the authorities can take the necessary decisions for their rehabilitation or retrofit. A new approach to the problem is the application of methods that fall within the field of Artificial Neural Networks (ANNs). In this paper, an application of ANNs is attempted to predict the level of seismic damage in reinforced concrete frames. For this purpose, 27 frames with different structural characteristics were selected, designed and analyzed by nonlinear dynamic analysis. Then, ANNs were used to test their ability to reliably predict the level of seismic damage. The parameters that configure the networks were also investigated and their performance was evaluated using a number of metrics. The results showed that the optimal network can estimate the seismic damage level with significant reliability, provided that the training sample and the network modeling parameters are properly selected through a testing procedure.