  The knowledge gap in the expected and actual conditions of bridges has
created worldwide deficits in infrastructure service and funding challenges.
Despite rapid advances over the past four decades, sensing technology is still
not a part of bridge inspection protocols. Every time a vehicle with a mobile
device passes over a bridge, there is an opportunity to capture potentially
important structural response information at a very low cost. Prior work has
shown how bridge modal frequencies can be accurately determined with
crowdsourced smartphone-vehicle trip (SVT) data in real-world settings.
However, modal frequencies provide very limited insight on the structural
health conditions of the bridge. Here, we present a novel method to extract
spatial vibration characteristics of real bridges, namely, absolute mode
shapes, from crowdsourced SVT data. These characteristics have a demonstrable
sensitivity to structural damage and provide superior, yet complementary,
indicators of bridge condition. Furthermore, they are useful in the development
of accurate mathematical models of the structural system and help reconcile the
differences between models and real systems. We demonstrate successful
applications on four very different bridges, with span lengths ranging from
about 30 to 1300 meters, collectively representing about one quarter of bridges
in the US. Supplementary work applies this computational approach to accurately
detect simulated bridge damage entirely from crowdsourced SVT data in an
unprecedentedly timely fashion. The results presented in this article open the
way towards large-scale crowdsourced monitoring of bridge infrastructure.
