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.
Vibration-based damage identification (VBDI) techniques rely on the fact that damage in a structure reduces its stiffness and alters its global vibration characteristics. Measurement of changes in the vibration characteristics can therefore be used to determine the damage in the structure. Although VBDI offers several advantages, most of the available damage identification algorithms fail when applied to practical structures due to the effect of measurement errors, need to use incomplete mode shapes, mode truncation, and the nonunique nature of the solutions. This article presents a new robust two-step algorithm that uses the modal energy-based damage index to locate the damage and an artificial neural network technique to determine the magnitude of damage. The proposed algorithm is applied to detect simulated damage in a finite element model of a girder and a similar model of a real bridge named Crowchild Bridge located in Alberta, Canada. The results show that the proposed algorithm is quite effective in identifying the location and magnitude of damage, even in the presence of measurement errors in the input data.
Hidden damage severely threatens the safety of structures due to its invisibility and indistinguishability. Debonding is typical hidden damage in steel-reinforced bridges. Detection of multiple debondings in steel-reinforced bridges poses a challenge for traditional nondestructive methods that require damage location as prior knowledge, but this condition is usually not satisfied for debonding located within the bridge. Differing from existing studies, this study explores a vibrational method of detecting multiple debondings needing no prior knowledge, that uses a scanning laser vibrometer to acquire densely-sampled operating deflection shapes of bridges. The operating deflection shape carries richer information than mode shapes for damage characterization. Nevertheless, densely-sampled deformed quantities are commonly susceptible to noise when used to identify damage. To detect multiple debondings, a new feature is developed, the wavelet-transform curvature operating deflection shape. This feature is used to identify multiple debondings in a steel-reinforced concrete slab dismantled from a bridge, clearly demonstrating the strengths of suppressing noise, intensifying the signatures of debonding, and requiring no prior knowledge of damage location. The proposed method holds promise for detecting multiple hidden damage in various structures besides bridges.
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