Structural Engineering and Mechanics

Volume 30, Number 4, 2008, pages 445-466

DOI: 10.12989/sem.2008.30.4.445

A new statistical moment-based structural damage detection method

J. Zhang, Y. L. Xu, Y. Xia and J. Li

Abstract

This paper presents a novel structural damage detection method with a new damage index based on the statistical moments of dynamic responses of a structure under a random excitation. After a<br />brief introduction to statistical moment theory, the principle of the new method is put forward in terms of a single-degree-of-freedom (SDOF) system. The sensitivity of statistical moment to structural damage is discussed for various types of structural responses and different orders of statistical moment. The formulae for statistical moment-based damage detection are derived. The effect of measurement noise on damage detection is ascertained. The new damage index and the proposed statistical moment-based damage detection method are then extended to multi-degree-of-freedom (MDOF) systems with resort to the leastsquares method. As numerical studies, the proposed method is applied to both single and multi-story shear buildings. Numerical results show that the fourth-order statistical moment of story drifts is a more sensitive indicator to structural stiffness reduction than the natural frequencies, the second order moment of story drift, and the fourth-order moments of velocity and acceleration responses of the shear building. The fourth-order statistical moment of story drifts can be used to accurately identify both location and severity of structural stiffness reduction of the shear building. Furthermore, a significant advantage of the proposed damage detection method lies in that it is insensitive to measurement noise.

Key Words

damage detection; statistical moment; sensitivity; measurement noise.

Address

J. Zhang, Y. L. Xu and Y. Xia: Dept. of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China<br />J. Li: Dept. of Building Engineering, Tongji University, Shanghai 200092, China