Structural damage detection using dynamic measurements has led to the development of several techniques in the last decades. Most of these methods associate modal variations of the structure to damage like methods based on strain energy deviation, methods based on changes in curvature mode shapes, flexibility matrix analysis, etc. Although these techniques aforementioned are mostly efficient to identify structural alterations in numerical models, they have difficulties in practical applications with experimental data. Thus, hybrid methods to detect the presence of damage directly from raw dynamic measurements in addition to structural modal characteristics can be a promising field of research, involving strategies based on artificial intelligence and higher-order statistics. This work aims to present the preliminary results of a hybrid method to detect structural damage. Using modal data and also higher-order statistics of structural time histories as inputs of artificial intelligence algorithms, the viability of the proposed methodology is initially evaluated. Two applications are analyzed: a simply supported numerical beam and an experimental tested prototype concrete slab. The good results achieved motivate the continuous development of the proposed hybrid method
