基于区间分析的框架结构不确定性损伤识别方法

考虑实际结构易受荷载、环境温度和测试噪声等不确定性因素的影响,笔者基于区间分析原理提出框架结构不确定性损伤识别方法。利用测试的结构加速度响应数据,建立向量自回归模型,并采用其系数矩阵主对角线的马氏距离作为损伤特征指标。基于粒子群算法建立区间优化求解方法,并与传统的区间组合法和区间叠加法对比。通过提出的区间重叠率指标和区间名义值分别实现损伤定位和损伤程度的识别。数值模拟和实验室框架结构试验结果表明,区间分析能在测试数据较少时实现损伤识别,为损伤识别在实际结构中的应用提供了理论基础和技术手段。

Damage detection considering uncertainties based on interval analysis

A damage detection method for uncertainty quantification using interval analysis is explored in this study. A vector auto-regression (VAR) model is established on the basis of the structural acceleration response data from the test. The Mahalanobis distance is extracted from the main diagonal of the coefficient matrix of VAR model and is adopted as the damage characteristic index. On the basis of particle swarm optimization (PSO), an interval optimization method is established, and its performance is compared with two traditional methods using a non-convex function. The damage location and damage degree are identified by interval overlap index and interval nominal value respectively. Both the numerical simulation and a laboratory frame structure test show that the presence and the severity of damage can be confidently detected even with a few data.