Neuro-fuzzy techniques for the classification of earthquake damages in buildings

The identification of damages produced by severe earthquakes on constructions is important for several reasons such as public safety, economical recourses management, infrastructure and urban planning. After the manifestation of an earthquake, engineers have to evaluate the safety of existing structures and decide the actions to be taken. In this study two techniques are proposed for automatic damage classification in buildings. The inherent information contained in accelerograms is described by 20 seismic parameters. Two classification models of earthquake damages based on artificial neural networks and neuro-fuzzy systems were designed. Furthermore, they were tested for their effectiveness to classify structural, architectural, mechanical–electrical-plumbing and contents damages. The proposed systems were trained and tested with three reinforced concrete frame structures. Results show correct classification rates up to 98%. According to these classification rates these techniques are proven a suitable tool for classification of earthquake damages in structures.