neural networks for the prediction of seismic damage in reinforced concrete structures

The possible seismic damage to structures can be evaluated using either vulnerability curves or non-linear dynamic analyses. however, the nonlinear dynamic analyses are time-consuming and the vulnerability curves can only be applied to a certain type of structures. there have been approaches to find correlation between several ground motion parametersand damage indices for buildings in the past (lönhoff, 2017; kwon, 2006; de lautour, 2009).for this purpose, this paper deals with the idea of using artificial neural networks to significantly simplify thecalculations. in fact, neural networks are able to find complex correlations in large data sets. since the aim of theinvestigation is to determine the damage caused by an earthquake, suitable acceleration time curves must first be selected as training data. the correct selection of input data is crucial for the successful training of an artificial neural network. for the analysis of the damage, only strong earthquakes with a distance of more than 15 km were used, because near-field earthquakes show special damage behaviour due to the peak loads.