  Using low-cost piezoelectric sensors to sense structural vibration exhibits
great potential in augmenting structural engineering, which is yet to be
explored in the literature to the best of our knowledge. Examples of such
unexplored augmentation include classifying diverse structures (such as
building, flyover, foot over-bridge, etc.), and relating the extent of
vibration generated at different height of a structure and the associated
height. Accordingly, to explore these cases, we develop a low-cost
piezoelectric sensor-based vibration sensing system aiming to remotely collect
real vibration data from diversified civil structures. We dig into our
collected sensed data to classify five different types of structures through
rigorous statistical and machine-learning-based analyses. Our analyses achieve
a classification accuracy of up to 97% with an F1 score of 0.97. Nonetheless,
in the rarely explored time domain, our analyses reveal a novel modality of
relation between vibration generated at different heights of a structure and
the associated height, which was explored in the frequency domain earlier in
the literature with expensive sensors.
