This paper presents an efficient inverse global optimization approach for damage identification of plate-like structures. In this approach, the damage identification process is performed by minimizing an objective function based on modal parameters of CFRP laminated structures. The identification process entails two steps: i) the direct problem is modeled using the finite element method. Damage is induced into the two different situations, first as a variation in physical properties, i.e., delamination, as a variation in stiffness and also as a variation in the grommet properties, for example small circular holes; ii) For solving the optimization problem, an enhanced SunFlower Optimization (SFO) algorithm is applied in the inverse problem methodology. The SFO metaheuristic algorithm has its biological operators optimized by mixture design method. The efficiency of the proposed identification is investigated through two numerical examples for laminated composite plates where Genetic Algorithm, SFO and an improved SFO algorithm are compared. The obtained results indicate that the proposed Structural Health Monitoring method can successfully identify the location and the severity of small induced damage cases in the laminated composite plate. In addition, the improved algorithm was shown to be more efficient and accurate than the widely known and applied Genetic Algorithm.

本文提出了一种有效的逆全局优化方法，用于板状结构的损伤识别。在这种方法中，通过基于CFRP层压结构的模态参数使目标函数最小化来执行损伤识别过程。识别过程包括两个步骤：i）使用有限元方法对直接问题进行建模。在两种不同情况下会导致损坏，首先是物理特性（即分层）的变化，刚度的变化以及索环特性的变化（例如小圆孔）；ii）为了解决优化问题，在逆问题方法中应用了增强的SunFlower优化（SFO）算法。SFO元启发式算法通过混合设计方法优化了其生物运算符。通过两个数值示例研究了复合板的识别效率，比较了遗传算法，SFO和改进的SFO算法。获得的结果表明，所提出的结构健康监测方法可以成功地识别层压复合板中小的诱发损伤案例的位置和严重性。另外，改进算法比广为人知和应用的遗传算法更加有效和准确。