Piezo-electric energy harvesters (PEHs) are used to produced energy from mechanical vibrations, for instance, bridge vibrations caused by passing vehicles. This energy can be supplied to small electronic devices, such as sensors used for structural health monitoring. In addition to energy generation, PEH can be used as a sensor, since the voltage signal produced by a PEH contains information about the source of vibration (e.g. speed and mass of the passing vehicle or the state of the bridge (healthy or damaged)). Then a dedicated sensor can be eliminated from the sensing system, and the energy produced by a PEH can be used to power other parts of the system more efficiently.

The aim of this project is to design bi-functional PEHs, optimised to simultaneously produce maximum energy and accurately sense quantities of interest (e.g. healthy/damaged state of the bridge). For this aim, we use state-of-the-art models and methods of computational mechanics paired with optimisation algorithms and machine learning techniques. A particular task of the ToR project is to produce a large database of CWT-images, using in-house Matlab codes for various traffic characteristics converted to the voltage signals by PEH models with various parameters. Then these CWT-images will be used to train a deep neural network to recognise bridge damage in terms of location and severity, and the accuracy of this voltage-based sensing framework will be quantified. Finally, an optimisation algorithm will be added to identify PEH devices with maximum harvesting and sensing capabilities.


Civil and Environmental Engineering

Research Area

Computational mechanics

The project is desktop-based. Access to Matlab and NCI HPC (supercomputer) will be provided. The project will be conducted in collaboration with the team of three PhD students, one postdoc and two staff members.

As an outcome of this project, it is expected to produce a large dataset of images, which will be used for various studies.

Senior Lecturer
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