Structural stress prediction based on deformations using artificial neural networks trained with artificial noise

Abstract

AbstractArtificial Neural Networks (ANN) have found application for multiple problems in structural mechanics and civil engineering. In the new approach developed here, ANNs are used for the determination of the maximum stress resultants in a structure on the basis of monitored displacements. Initially, a simple supported beam subjected up to two vertical forces is considered. The beam is solved analytically for different combinations of load positions and magnitudes defined by Monte‐Carlo sampling. The resulting beam deflections and maximum stress resultants are employed for the training of feedforward neural networks in order to generate metamodels able to predict the maximum bending moment knowing only a few beam input deflections. Subsequently, the concept is applied to a real beam experiment, where a reinforced concrete beam has been tested. It is shown, that the perfect correlation of the beam displacements used as inputs for the ANN training leads to extrapolations when the metamodel is applied to the measured displacements of the real experiment, which are not perfectly correlated. This is source of wrong predictions when the network is applied to these data. The introduction of artificial noise in the synthetic input data in a controlled way before training helps to overcome extrapolation and to increase repeatability and robustness of the metamodels. Various noise levels are applied to the synthetic data to monitor how the ANN performances change over increasing noise and the results along with the limitations of the approach are illustrated.