<p>Fabrication
technology and structural engineering states-of-art have led to a growing use
of slender structures, making them more susceptible to static and dynamic
actions that may lead to some sort of damage. In this context, regular
inspections and evaluations are necessary to detect and predict structural
damage and establish maintenance actions able to guarantee structural safety
and durability with minimal cost. However, these procedures are traditionally
quite time-consuming and costly, and techniques allowing a more effective
damage detection are necessary. This paper assesses the potential of Artificial
Neural Network (ANN) models in the prediction of damage localization in
structural members, as function of their dynamic properties – the three first
natural frequencies are used. Based on 64 numerical examples from damaged
(mostly) and undamaged steel channel beams, an ANN-based analytical model is
proposed as a highly accurate and efficient damage localization estimator. The
proposed model yielded maximum errors of 0.2 and 0.7 % concerning 64 numerical
and 3 experimental data points, respectively. Due to the high-quality of results, authors’ next step is
the application of similar approaches to entire structures, based on much
larger datasets.</p>