基于静态结构识别方法,提出了服役结构损伤探测及状态评估的概率分析方法。首先通过有限元法把结构离散成用基本参数及核心矩阵表示的分析模型,根据两种分析模型和实际结构间的误差定义,用 Gauss-Newton法推导了在不完全测量情况下两种参数识别方法。在设计的测量情况下,进一步采用 Monte Carlo 法模拟测量数据,详细分析和比较这两种算法在有测量误差时的有效性和稳定性问题。第一种算法中的敏感矩阵不受测量误差的影响而具有比较好的性能,在确定了其识别结果的概率特性后,引入假设试验,探测和评估损伤的位置及程度。数字模拟显示了这一方法的有效性和准确性。
A statistical damage detection and condition assessment scheme for existing structures is developed. First a virtual work error estimator is defined to express the discrepancy between a real structure and its analytical model, with which a system identification algorithm is derived by using the improved Newton method. In order to investigate its properties in the face of measurement errors, the Monte Carlo method is introduced to simulate the measured data. Based on the identified results, their statistical distributions can be assumed, the status of an existing structure can be statistically evaluated by hypothesis tests. A 5-story, two-bay steel frame is used to carry out numerical simulation studies in detail, and the proposed scheme is proved to be effective.
