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On-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures

机译:随机建筑结构识别/损伤检测与结构可靠性评估的在线集成

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摘要

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.
机译:最近,已经提出了一些具有不确定性的结构的综合结构识别/损伤检测和可靠性评估。但是,这些技术适用于结构识别和可靠性评估的离线综合。本文基于扩展卡尔曼滤波器的递归公式,研究了随机识别结构的结构识别/损伤检测和可靠性评估的在线集成。根据不确定变量,通过泰勒级数展开结构极限状态,以获得概率密度函数(PDF)。研究了仅具有一个极限状态函数的结构部件可靠性和具有多个极限状态函数的系统可靠性。然后,扩展为采用作者提出的最新扩展卡尔曼滤波器(EKF-UI),用于未知激励下随机建筑物结构识别/损伤检测和结构可靠性评估的在线集成。数值算例表明了该方法的有效性。将结构部件可靠性和结构系统可靠性的评估结果与蒙特卡洛模拟进行了比较,以验证所提出方法的性能。

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