Monitoring structural integrity has become increasingly important in the field of civil engineering. A huge part of cities depend of civil engineer infrastructures such as bridges, dams and buildings to provide several benefits to modern society. On the other hand, even the most conservative design cannot resist the power of time. A good preventive maintenance routine don’t let go of the need in constant verification for faults because in some cases that could lead to large scale catastrophes involving big material and life costs. Thanks to technology development over the last decades it was possible to search and create many powerful tools that could help those kind of problems. The objective of this thesis is to assess on the application of Artificial Intelligence Methods to detect damage on beams. The formulation uses modal parameters of a structure to verify the presence of damage related to the reduction of stiffness of a section. More specifically, the methods presented on this study are data-driven, so first a database for training and validating the AI methods were generated in a Python program within the finite element software Abaqus. The modal parameters analyzed were the first five natural frequencies of a beam. It was possible to evaluate the performance of the AI methods when classifying a beam with or without damage on different metrics. Finally, a parametric comparison was made between the Artificial Intelligence methods.