Computer-Aided Civil and Infrastructure Engineering, 2001
Abstract: Supporting structures are subject to damage processes during their service lifetime. Da... more Abstract: Supporting structures are subject to damage processes during their service lifetime. Damage may occur in the form of material or structural damage during the loading process. The parameters for describing damage are modelled as fuzzy values in the ...
Computer-Aided Civil and Infrastructure Engineering, 2010
ABSTRACT In this article, an approach is introduced which permits the numerical prediction of fu... more ABSTRACT 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 α-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.
Computer-Aided Civil and Infrastructure Engineering, 2001
Abstract: Supporting structures are subject to damage processes during their service lifetime. Da... more Abstract: Supporting structures are subject to damage processes during their service lifetime. Damage may occur in the form of material or structural damage during the loading process. The parameters for describing damage are modelled as fuzzy values in the ...
Computer-Aided Civil and Infrastructure Engineering, 2010
ABSTRACT In this article, an approach is introduced which permits the numerical prediction of fu... more ABSTRACT 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 α-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.
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