Structural Engineering and Mechanics

Volume 63, Number 6, 2017, pages 789-797

DOI: 10.12989/sem.2017.63.6.789

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

Ying Lei, Longfei Wang, Lanxin Lu and Dandan Xia

Abstract

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.

Key Words

structural identification; damage detection; uncertainties; probability; reliability evaluation; on-line; Integration; extended Kalman filter; partial measurements

Address

Ying Lei, Longfei Wang, Lanxin Lu: School of Architecture and Civil Engineering, Xiamen University, Xiamen, 361005, China Dandan Xia: School of Civil & Architecture Engineering, Xiamen University of Technology, Xiamen, 361024, China