Damage assessment using neural networks

In this paper, a method of damage assessment based on neural networks (NNs) is presented and applied to the Steelquake structure. The method is intended to assess the overall damage at each floor in composite frames caused by seismic loading. A neural network is used to calibrate the initial undamaged structure, and another to predict the damage. The natural frequencies of the structure are used as inputs of the NNs. The data used to train the NNs were obtained through a finite element (FE) model. Many previous approaches have exhibited a relatively poor capacity of generalisation. In order to overcome this problem, a FE model more suitable to the definition of damage is tried herein. Further work in this paper is concerned with the validation of the method. For this end, the damage levels of the structure were obtained through the trained NNs from the available experimental modal data. Then, the stiffness matrices of the structure predicted by the method were compared with those identified from pseudo-dynamic tests. Results are excellent. The new FE model definition allows the NNs to have a much better generalisation. The obtained values of the terms of the stiffness matrix of the undamaged structure are almost exact when comparing with the experimental ones, while the absolute differences are lower than 8.6% for the damaged structure. (C) 2003 Elsevier Science Ltd. AN rights reserved.