Damage Identification of Substructure Using Cross-Correlation Function

建筑结构在长期服役过程中不可避免地会产生结构损伤，因此需要对结构进行健康监测。传统的整体结构损伤识别在面对大型结构时，往往会面临计算效率低下，计算精度不足的困境，而子结构方法能够有效地降低这两方面的难度。此外，大部分建筑结构所受激励情况未知进一步提高了损伤识别的难度。互相关函数方法能够在不测量环境激励的情况下完成损伤识别，为解决此类问题提供了新的思路。鉴于此，本文提出了基于互相关函数的子结构损伤识别方法，并通过桁架子结构进行数值模拟，验证了所提方法的有效性。Structural damage is inevitable during long-term service of building structures, therefore health monitoring of the structure is necessary. Traditional overall structural damage identification often faces the dilemma of low computational efficiency and insufficient computational accuracy when facing large structures, while substructure methods can effectively reduce the difficulty of these two aspects. In addition, the unknown excitation conditions of most building structures further increase the difficulty of damage identification. The cross-correlation function method can complete damage identification without measuring environmental stimuli, providing a new approach to solving such problems. In view of this, this article proposes a substructure damage identification method based on cross-correlation functions and verifies the effectiveness of the proposed method through numerical simulations of truss structures.