摘要
为了解决空间钢架结构非线性损伤的识别问题,提出了一种基于自回归条件异方差(auto regressive conditional heteroskedastic,ARCH)模型残差偏移距离的损伤识别方法。文章首先描述ARCH模型的基本理论,并给出了ARCH模型建模的定阶方法和模型参数的估计方法;接着分析结构非线性损伤的特性;然后,考虑到传统的非线性识别指标难以运用到空间钢架结构,提出基于ARCH模型的欧氏距离指标和改进的残差偏移距离指标识别钢结构损伤位置。最后,使用一个8层剪切结构数值模拟验证了2个损伤指标的有效性,并且将文章提出的指标运用于输电塔钢架模型的非线性损伤识别试验中。模拟和试验结果表明,传统的非线性损伤识别指标难以直接运用于钢架的损伤识别,而建议的欧氏距离指标和残差偏移距离指标可以较好地识别出钢架结构的非线性损伤。
To solve the problem of time-domain nonlinear damage identification of Three-dimensional steel frame structure, a damage identification method based on the residual metric index of the ARCH(Auto Regressive Conditional Heteroskedastic) model is proposed.Firstly, the basic principle is described, and the estimation methods of the order and parameters of ARCH model are given.Secondly, the characteristics of the structure’s nonlinear damage are analyzed.Considering that the traditional nonlinear identification index is difficult to be applied to the steel frame structure, the Euclidean distance index based on the ARCH model and the improved residual metric index are proposed to identify the damage location of the steel frame structure.Finally, the numerical simulation of an eight-storey shear structure is conducted to verify the effectiveness of the two damage indicators.And the proposed indexes are applied to the nonlinear damage identification of the transmission tower model.The simulation and experiment results show that the traditional nonlinear damage identification index is difficult to be directly applied to the steel frame structure damage identification.However, the proposed Euclidean metric index and residual metric index are sensitive and can be used to identify the nonlinear damage of the steel frame structure.
作者
张锋
郭惠勇
Zhang Feng;Guo Huiyong(School of Civil Engineering,Chongqing University,Chongqing 400045,China;Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Chongqing 400045,China)
出处
《土木工程学报》
EI
CSCD
北大核心
2021年第2期65-73,共9页
China Civil Engineering Journal
基金
国家重点研发计划项目(2018YFC0809400)
国家自然科学基金(51578094)。
