Performance of Vibration-based Techniques for the Identification of Structural Damage

Abstract

Early detection of damage is of special concern for civil engineering structures. If not identified in time, damage may have serious consequences, both safety related and economic. The traditional methods of damage detection include visual inspection or instrumental evaluation. A comparatively recent development in the health monitoring of civil engineering structures is vibration-based damage detection. Vibration characteristics of a structure, that is, its frequencies, mode shapes, and damping are directly affected by the physical characteristics of the structure including its mass and stiffness. Damage reduces the stiffness of the structure and alters its vibration characteristics. Therefore, measurement and monitoring of vibration characteristics should theoretically permit the detection of both the location and severity of damage. However, in practice, a number of difficulties persist in vibration-based damage identification. As a result, most of the damage identification algorithms fail when applied to practical civil engineering structures. This article presents a survey of some of the more commonly used algorithms and describes the conditions under which they may or may not work. The success of individual algorithms is measured through computer simulation studies. It may, however, be noted that additional practical difficulties that cannot entirely be reproduced through computer simulation exist, which makes vibration-based damage identification a challenging field with many unanswered questions.

Consistent Regularization for Damage Detection with Noise and Model Errors

“…Many of the methods are based on the observation of dynamic behavior of a structure (Doebling et al [1], Brincker et al [2], Maeck and DeRoeck [3]), among which the sensitivity approach via a model-updating technique is commonly accepted and applied extensively in the engineering industry because of its clear mathematical background and quantitative indications. However, this type of method is weak at accommodating the influence of measurement errors, leading to illconditioned problems, as demonstrated by Friswell et al [4] and Humar et al [5]. This shortcoming means that the existence and uniqueness of the solution is not ensured and numerical instability is likely to take place in the course of the solution process [6,7].…”

Monitoring and Damage Detection in Structural Parts of Wind Turbines

“…Reviews and case studies have been written by Sohn et al (2004), Carden & Fanning (2004), Montalvão et al (2006), Humar et al (2006), Fan & Qiao (2010) and, with special emphasis on the monitoring of wind turbines, by Ciang et al (2008) and Hameed et al (2007). The large size of wind turbines and the difficult accessibility complicates maintenance and repair work.…”

Effect of frequency domain attributes of wavelet analysis filter banks for structural damage localisation using the relative wavelet entropy index

“…In particular, the 8-bay simply supported aluminium truss of Figure 6 is considered, which is based on Humar et al (2006) as a benchmark structure to assess the performance of various vibration-based techniques for damage detection. The truss comprises 100 tubular members and each bay is a cube with 707mm long side.…”

“…These damage-sensitive indices may coincide with the dynamic/modal properties (e.g., natural frequencies) or mechanical properties (e.g., stiffness coefficients) of the monitored structure, or be derived from them (e.g., modal curvatures, strain energy, etc.) (e.g., Humar et al 2006). Alternatively, datadriven damage indices, not amenable to any physical/structural interpretation (but related to the physics of the problem), have also been considered in conjunction with statistical signal processing techniques for the purpose at hand.…”