改进的正则化模型修正方法在结构损伤识别中的应用
STRUCTURE DAMAGE IDENTIFICATION BY FINITE ELEMENT MODEL UPDATED WITH IMPROVED TIKHONOV REGULARIZATION
-
摘要: 基于灵敏度分析的有限元模型修正方法与Tikhonov 正则化方法相结合,可以有效抑制实测模态参数中噪声的影响,正确识别结构损伤,但也存在着识别结果过度光滑的缺陷。通过在Tikhonov 罚函数项中引入光滑函数,改善Tikhonov 正则化方法对非光滑解的描述能力,在保持识别算法鲁棒性的同时,提高模型修正方法对于结构损伤的识别精度。以简支梁模型为例的损伤识别数值模拟表明,该文方法不仅能扩大正则化参数的可选择范围,还能显著降低噪声对识别结果的干扰,提高单元损伤程度的识别精度。Abstract: The sensitivity-based finite element model updated with classical Tikhonov regularization can alleviate the ill-conditioning in solving the damage identification problems, and suppress the influence of noise in the measured model parameters. However the introduction of Tikhonov regularization may lead to an over-smooth solution. In order to improve the identification of non-smooth solution, smooth function is introduced in Tikhonov punishment function to enhance robustness and accuracy of the structure damage identification algorithm. The numerical simulations show that sensitivity-based model updated with improved Tikhonov regularization can reduce the influence of measurement noise effectively and identify the structure damages correctly.