ANN inverse analysis based on stochastic small-sample training set simulation

Authors:
Drahomír Novák;David Lehký
Affiliations:
Brno University of Technology, Faculty of Civil Engineering, Institute of Structural Mechanics, Veveri 95, 602 00 Brno, Czech Republic;Brno University of Technology, Faculty of Civil Engineering, Institute of Structural Mechanics, Veveri 95, 602 00 Brno, Czech Republic
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neural network (ANN). The identification parameters play the role of basic random variables with a scatter reflecting the physical range of potential values. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network. Once the network has been trained, it represents an approximation consequently utilized to solve the key task: To provide the best possible set of model parameters for the given experimental data. The efficiency of the approach is shown using numerical examples from civil engineering computational mechanics.