  Deciding how to optimally deploy sensors in a large, complex, and spatially
extended structure is critical to ensure that the surface pressure field is
accurately captured for subsequent analysis and design. In some cases,
reconstruction of missing data is required in downstream tasks such as the
development of digital twins. This paper presents a data-driven sparse sensor
selection algorithm, aiming to provide the most information contents for
reconstructing aerodynamic characteristics of wind pressures over tall building
structures parsimoniously. The algorithm first fits a set of basis functions to
the training data, then applies a computationally efficient QR algorithm that
ranks existing pressure sensors in order of importance based on the state
reconstruction to this tailored basis. The findings of this study show that the
proposed algorithm successfully reconstructs the aerodynamic characteristics of
tall buildings from sparse measurement locations, generating stable and optimal
solutions across a range of conditions. As a result, this study serves as a
promising first step toward leveraging the success of data-driven and machine
learning algorithms to supplement traditional genetic algorithms currently used
in wind engineering.
