In this paper, a novel two-stage structural damage detection method using modal flexibility and strain energy-based index (MFSEBI) and modified teaching–learning-based optimization (MTLBO) algorithm is proposed. In the first stage, a novel damage index (MFSEBI) is proposed based on the combination of two structural modal properties including modal strain energy of elements and diagonal members of the structural flexibility matrix to identify the suspected damaged elements. The performance of this indicator is compared with the performance of an indicator derived from the modal strain energy. As the change in the structural modal properties is also affected by environmental variations, in this study, the effect of varying environmental conditions on the performance of the MFSEBI is also examined. In the second stage, the modal response of the structure is used and subsequently updated by MLTBO to estimate the damage extents of the suspected elements. At this stage, the MTLBO algorithm is proposed by enhancing the teaching and learning quality of the TLBO algorithm using new updating mechanisms of each learner position. The performance of the MTLBO and TLBO algorithms is compared in terms of the accuracy of the results and convergence rate. The results of three numerical examples indicate a better performance of the MFSEBI index in damage localization using a smaller number of mode shapes. These results also demonstrate the higher accuracy of the estimated damage extents and the faster convergence rate of the objective function using MTLBO, even with the consideration of measurement noise effects.

中文翻译：

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环境变化下基于模态柔度和应变能的新型损伤指数结构损伤检测的改进教学优化算法

在本文中，提出了一种使用基于模态柔度和应变能的指数（MFSEBI）和改进的基于教学的优化（MTLBO）算法的两阶段结构损伤检测方法。在第一阶段，基于两种结构模态特性的组合，包括单元的模态应变能和结构柔性矩阵的对角线成员，提出了一种新的损伤指数（MFSEBI），以识别可疑的损伤单元。将该指标的性能与从模态应变能导出的指标的性能进行比较。由于结构模态特性的变化也受环境变化的影响，在本研究中，还研究了不同环境条件对 MFSEBI 性能的影响。在第二阶段，使用结构的模态响应并随后由 MLTBO 更新以估计可疑元素的损坏程度。在这个阶段，MTLBO 算法被提出，通过使用每个学习者位置的新更新机制来提高 TLBO 算法的教学和学习质量。MTLBO 和 TLBO 算法的性能在结果的准确性和收敛速度方面进行了比较。三个数值例子的结果表明 MFSEBI 指数在使用较少数量的模态振型的损伤定位方面具有更好的性能。这些结果还表明，即使考虑了测量噪声影响，使用 MTLBO 估计损伤程度的精度更高，目标函数的收敛速度也更快。