基于克隆选择和粒子群混合算法的导墙结构损伤识别

Damage identification method for guide wall structures based on a hybrid algorithm of clonal selection and particle swarm optimization

导墙结构长期受到水流和风等交变荷载作用,容易产生结构损伤。由于环境激励输入的未知性以及测试条件和精度的限制,使得环境激励下大型水工结构的损伤诊断遇到了很大困难。基于此,提出一种利用实数编码克隆选择和粒子群混合算法优化模态频率指标的导墙结构损伤诊断方法。该方法仅需可测性强的低阶模态频率,非常适合于环境激励条件下的大型水工结构的无损动态损伤检测。将该诊断方法应用于某导墙的损伤识别中,证明该方法在算法全局寻优性能和识别准确性上均有较大优势,可尝试在各类水工结构的损伤诊断中推广应用。

The guide wall structure in hydraulic engineering is subjected to long-term complicated loads, such as, alternative water pressure and wind pressure, they may lead to the damage of structures. However, damage detection is difficult to implement in large hydraulic structures under ambient excitation because of the uncertainty of ambient excitation and the limitation of the test condition and precision. Here, a new damage detection method using a real encoding hybrid algorithm of clonal selection and particle swarm optimization to optimize the modal frequency index was proposed for guide wall structures. The proposed method only needed lower modal frequencies, thus it was suitable for nondestructive dynamic damage detection of large hydraulic structures under ambient excitation. Taking a certain guide wall structure as an example, the results showed that this method has advantages in the global searching performance and identification accuracy; the proposed method is effective and can be applied in many types of large hydraulic structures.