Structures that are difficult to maintain and access need to have an efficient and robust process for continuous monitoring. Such monitoring through damage detection and identification studies is present in several engineering applications as it allows that corrective measures be applied in order to guarantee the structural safety of a given structure, machine or equipment. In particular, laminated composite materials, often used in aeronautical structures, have a complex failure mechanism where delamination or cracks in these materials are often not visible on the surface. Thus, the use of optimization methods for the characterization of damages in these materials becomes relevant. In this study, both the metaheuristic sunflower optimization, the artificial neural networks and the response surface method were used to solve an inverse crack identification problem. The crack was modeled as a thin elliptical hole in a rectangular laminated plate numerically modeled using the finite element method. As a result of the methods used, different approaches to the problem were obtained that present reliable shape, size and position identification of a crack sized between 3 and 30 mm. The results showed substantial and promising results in the uses of both metaheuristic techniques and artificial neural networks. However, neural networks have a certain competitive advantage over optimization techniques as long as the data that feeds the model present a certain level of quantity and quality. Results obtained were able to identify all damage parameters (location, extension and orientation), with errors less than 1%.

难以维护和访问的结构需要有一个高效而稳健的流程来进行持续监控。这种通过损伤检测和识别研究进行的监测存在于多种工程应用中，因为它允许采取纠正措施以保证给定结构、机器或设备的结构安全。特别是，经常用于航空结构的层压复合材料具有复杂的失效机制，这些材料的分层或裂纹通常在表面上看不到。因此，使用优化方法来表征这些材料中的损伤变得很重要。在本研究中，元启发式向日葵优化，人工神经网络和响应面方法被用来解决逆裂纹识别问题。裂纹被建模为矩形层压板中的薄椭圆孔，使用有限元方法进行数值模拟。由于所使用的方法，获得了解决该问题的不同方法，这些方法提供了尺寸在 3 到 30 毫米之间的裂纹的可靠形状、尺寸和位置识别。结果表明，元启发式技术和人工神经网络的使用都取得了实质性和有希望的结果。然而，只要提供给模型的数据具有一定的数量和质量水平，神经网络就比优化技术具有一定的竞争优势。获得的结果能够识别所有损坏参数（位置、延伸和方向），