Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior

In this article, an approach is introduced which permits the numerical prediction of future structural responses in dependency of uncertain load processes and environmental influences. The approach is based on recurrent neural networks trained by time-dependent measurement results. Thereby, the uncertainty of the measurement results is modeled as fuzzy processes which are considered within the recurrent neural network approach. An efficient solution for network training and prediction is developed utilizing alpha-cuts and interval arithmetic. The capability of the approach is demonstrated by means of the prediction of the long-term structural behavior of a reinforced concrete plate strengthened by a textile reinforced concrete layer.