Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0

Crack detection in a rotating shaft using artificial neural networks and PSD characterisation

Peer reviewedPostprin

Vibration serviceability of footbridges under human-induced excitation : a literature review

Increasing strength of new structural materials and longer spans of new footbridges, accompanied with aesthetic requirements for greater slenderness, are resulting in more lively footbridge structures. In the past few years this issue attracted great public attention. The excessive lateral sway motion caused by crowd walking across the infamous Millennium Bridge in London is the prime example of the vibration serviceability problem of footbridges. In principle, consideration of footbridge vibration serviceability requires a characterisation of the vibration source, path and receiver. This paper is the most comprehensive review published to date of about 200 references which deal with these three key issues. The literature survey identified humans as the most important source of vibration for footbridges. However, modelling of the crowd-induced dynamic force is not clearly defined yet, despite some serious attempts to tackle this issue in the last few years. The vibration path is the mass, damping and stiffness of the footbridge. Of these, damping is the most uncertain but extremely important parameter as the resonant behaviour tends to govern vibration serviceability of footbridges. A typical receiver of footbridge vibrations is a pedestrian who is quite often the source of vibrations as well. Many scales for rating the human perception of vibrations have been found in the published literature. However, few are applicable to footbridges because a receiver is not stationary but is actually moving across the vibrating structure. During footbridge vibration, especially under crowd load, it seems that some form of human–structure interaction occurs. The problem of influence of walking people on footbridge vibration properties, such as the natural frequency and damping is not well understood, let alone quantified. Finally, there is not a single national or international design guidance which covers all aspects of the problem comprehensively and some form of their combination with other published information is prudent when designing major footbridge structures. The overdue update of the current codes to reflect the recent research achievements is a great challenge for the next 5–10 years

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model