Vibration-based damage identification in composite beams

Abstract

Composite structures are widely used in bridge and building construction; a typical example is a bridge deck consisting of concrete slabs and steel beams, where shear connectors are used to connect the concrete slabs and steel beams to form a composite structure. The performance of a composite structure is well understood to depend not only on the properties of the primary components, such as the slabs and steel beams mentioned above but also, and importantly, on the properties and condition of the shear connectors. Therefore, in a structural health monitoring and damage identification process, it is imperative to distinguish the damages in the primary components and in the shear connectors. However, in the existing literature concerning damage assessment of composite structures, there is generally a lack of differentiation between the damages in the two distinctive groups of constituent entities, and oftentimes the damages are simply treated in terms of the gross flexural stiffness with the use of an equivalent Euler-Bernoulli beam. This could lead not only to a false identification of the actual severity of the damages but also misleading results in case severe damage to shear connections occur.

In this thesis, a comprehensive study on the damage assessment of composite beams, which to a large extent, represent the basic mechanics of composite structures, is conducted. Firstly, the basic mechanics governing the overall transverse flexural stiffness and the nominal sectional rigidity of a composite beam is investigated analytically, and the essential differences between the component beam damage and the shear connector damage on the distribution of the nominal flexural rigidity is examined by numerical simulations. On this basis, the feasibility of differentiating the two groups of damages from a damage identification process using vibration information, namely the natural frequencies and mode shapes, is investigated by means of a genetic algorithm (GA) - based finite element (FE) model updating.

Considering the limitation of the GA-based approach due to its global optimisation nature, a neural network-based method is then developed for the identification of damages in composite beams. In this development, the following sub-topics are systematically studied, i) The sensitivity of the dynamic properties of composite beams to the two groups of damages under different composite beam configurations; ii) The normalisation of the shear connector stiffness and component beam flexural stiffness and the corresponding utilisation in the neural network-based damage assessment scheme, and iii) Identification of multiple damages in a composite beam using a neural network-based damage assessment scheme. The wavelet packet node energy (WPNE), which could allow for the use of a single or few limited measurement points, is incorporated into the neural network-based damage identification scheme. It is shown that the WPNE-incorporated scheme can be efficient for identifying damages in composite beams. Finally, in conjunction with the analytical and numerical simulation studies, a laboratory experimental programme has also been conducted, and the feasibility of separating the shear connector damage and the beam section damage in a physical measurement environment is verified by employing the experimentally measured vibration data.

In conclusion, this thesis provides a comprehensive study on the damage assessment of composite structures, with a particular focus on distinguishing damages in the primary components and the shear connectors. The proposed methods, including the GA-based finite element model updating and neural network-based damage identification scheme, are shown to be effective in identifying damages in composite beams using vibration information. The laboratory experimental programme verifies the feasibility of the proposed methods in a physical measurement environment. The findings of this thesis will contribute to a better understanding of the behaviour of composite structures and improve the accuracy of structural health monitoring and damage assessment in practice.