Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data

Abstract

In the paper, an approach is described which permits the numerical, model-free prediction of uncertain time-dependent structural responses. Uncertain time-dependent structural actions and responses are modelled by means of fuzzy processes. The prediction approach is based on recurrent neural networks for fuzzy data trained by time-dependent results of measurements or numerical analyses. An efficient solution for network training and prediction is developed utilizing α-cuts and fuzzy arithmetic. The approach is verified using a fractional rheological model. The capability of the approach is demonstrated by predicting the long-term structural behaviour of reinforced concrete plates strengthened by textile reinforced concrete layers.