  Piezoelectric Energy Harvesters (PEHs) are typically employed to provide
additional source of energy for a sensing system. However, studies show that a
PEH can be also used as a sensor to acquire information about the source of
vibration by analysing the produced voltage signal. This opens a possibility to
create Simultaneous Energy Harvesting and Sensing (SEHS) system, where a single
piece of hardware, a PEH, acts as both, a harvester and as a sensor. This
raises a question if it is possible to design a bi-functional PEH device with
optimal harvesting and sensing performance. In this work, we propose a
bi-objective PEH design optimisation framework and show that there is a
trade-off between energy harvesting efficiency and sensing accuracy within a
PEH design space. The proposed framework is based on an extensive vibration
(strain and acceleration) dataset collected from a real-world operational
cable-stayed bridge in New South Wales, Australia. The bridge acceleration data
is used as an input for a PEH numerical model to simulate a voltage signal and
estimate the amount of produced energy. The numerical PEH model is based on the
Kirchhoff-Love plate and isogeometric analysis. For sensing, convolutional
neural network AlexNet is trained to identify traffic speed labels from voltage
CWT (Continuous Wavelet Transform) images. In order to improve computational
efficiency of the approach, a kriging metamodel is built and genetic algorithm
is used as an optimisation method. The results are presented in the form of
Pareto fronts in three design spaces.
