基于关联模态云推理算法的输电塔结构损伤识别

为了解决输电塔等工程结构在不确定因素干扰下的损伤识别问题,提出了一种基于关联模态的云推理算法。建立残余力基本方程,并分析了基于残余力向量的损伤识别原理;提出了基于残余力的云推理算法,给出了云模型的数字特征,分析了前件云发生器和后件云发生器,给出了基于灰云模型的定性规则库建立方法,并利用云规则组成了相应的云推理系统。考虑到残余力法易受测量噪声等不确定因素干扰的弱点,进一步提出了关联模态云推理算法,以提高损伤识别的精度和可靠性,并采用输电塔结构模型进行了损伤识别研究。数值计算结果表明,关联模态云推理算法可以较好地识别出结构损伤,其识别效果明显优于残余力向量法和残余力云推理算法。

Damage identification of transmission tower based on associated mode cloud reasoning algorithm

In order to identify the damage of transmission tower and other engineering structures under the influence of uncertain factors, a cloud reasoning algorithm based on associated mode is presented.Firstly, residual force equation is established and damage identification principle based on residual force vector is analyzed.Then, a cloud reasoning algorithm based on residual force is proposed.Numerical characteristics of cloud model are given, front cloud generator and rear cloud generator are analyzed, qualitative rules based on gray cloud model are proposed, and corresponding cloud reasoning system is composed by using cloud rules.Finally, given the drawback that residual force is susceptible to uncertain factors such as measurement noise, a cloud reasoning algorithm based on associated mode is presented to enhance the identification accuracy and reliability.And a transmission tower structure is applied to damage identification.The simulation results indicate that the cloud reasoning algorithm based on associated mode can well identify structural damage, and the identification results of the proposed algorithm are obviously superior to those of residual force vector method and the cloud reasoning algorithm based on residual force.