Adaptive neural networks for model updating of structures dating age range chart

Department of Mechanical Engineering, University of Chile, Beauchef 850, 8370448 Santiago, Chile Received 21 April 2013; Accepted 2 September 2013; Published 12 February 2014Academic Editor: Gyuhae Park Copyright © 2014 V. The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural damage given the structure's dynamic characteristics.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Damage assessment is a subject of great importance for several industry sectors as well as for the safety of citizens and can be applied to civil engineering structures (such as buildings or bridges), transport vehicles (such as airplanes, helicopters, trains, ships, or cars), and industrial equipment (such as mills, turbines, pumps, and boilers, among others).

The main problem of damage assessment is to ascertain the presence, location, and severity of structural damage given the structure’s dynamic characteristics.

Among all of the dynamic responses, the FRF is one of the easiest to obtain in real time because the in situ measurement is straightforward.

However, the number of spatial response locations and spectral lines is overly large for neural network applications.

The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms.

However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications.

The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms [1–5].

Model updating is an inverse method that identifies the uncertain parameters in a numerical model and is commonly formulated as an inverse optimization problem.

In inverse damage detection, the algorithm uses the differences between the models of the structure that are updated before and after the presence of damage to localize and determine the extent of damage.

The basic assumption is that the damage can be directly related to a decrease of stiffness in the structure.

Unlike many other damage detection methods that are often developed for specific quantities [9–11], in principle, ANN can be applied to any correlation coefficient that is sensitive to damage.

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