The increase in the number of new buildings and the existence of countless old buildings, whether small or large, call attention to the need for measures that maintain the quality, safety and useful life of the structures. Inspections and monitoring, regardless of the age of the building, are essential to detect the existence of damage, especially in its initial phase, avoiding its propagation or serious consequences that originate due to a collapse of the structure, due to the high degree deterioration and no recovery techniques. Based on these aspects, this dissertation has the general objective of detecting damage in structures using the machine learning approach, which integrates three techniques: initially the Ensemble Empirical Mode Decomposition (EEMD) is applied a processing of the signals and seeks to adapt them for the application of the Auto Regressive Model (AR) generating the attributes, which will serve as input patterns for the Support Vector Machine (SVM) classifier. The data used to apply the methods come from the modeling of bi-supported steel beams, intact and with damaged regions, by the SAP 2000 Structural Analysis Software. With reference to the creation of the structures by finite elements, two types of loads were applied . The first case of random loading acting in only one point of the beam and the second case with three simultaneous loads in three points of the beam. According to variations in the location and degree of severity of the damage, the study sought to assess the ability of the predictive models to classify the data correctly. In the analyzes with greater mass losses, the accuracy values are higher, decreasing according to the reduction of the damage geometry, as the signs of
displacement become similar to the integral structure. Regarding the number of loads, the method demonstrated better performance and accuracy in cases with three simultaneous loads.
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