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Over the last decade, concepts such as industry 4.0 and the Internet of
Things (IoT) have contributed to the increase in the availability and
affordability of sensing technology. In this context, Structural Health
Monitoring (SHM) arises as an especially interesting field to integrate and
develop these new sensing capabilities, given the criticality of structural
application for human life and the elevated costs of manual monitoring. Due to
the scale of structural systems, one of the main challenges when designing a
modern monitoring system is the Optimal Sensor Placement (OSP) problem. The OSP
problem is combinatorial in nature, making its exact solution infeasible in
most practical cases, usually requiring the use of meta-heuristic optimization
techniques. While approaches such as genetic algorithms (GA) have been able to
produce significant results in many practical case studies, their ability to
scale up to more complex structures is still an area of open research. This
study proposes a novel quantum-based combinatorial optimization approach to
solve the OSP problem approximately, within the context of SHM. For this
purpose, a Quadratic Unconstrained Binary Optimization (QUBO) model formulation
is developed, taking as a starting point the modal strain energy (MSE) of the
structure. The framework is tested using numerical simulations from two
relevant structures: a multi-story shear building and a Warren truss bridge.
The results obtained using the proposed framework are compared against
exhaustive search approaches to verify their approximation ratios. More
importantly, a detailed discussion of the current limitations of the technology
and the future paths of research in the area is presented to the reader.