Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges

Final Report (PDF)

Tech Brief (PDF)

Project TitleSmart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges
UniversityLehigh University
Principal Investigator(s)Shamim N. Pakzad
Funding Source(s) and Amounts Provided (by each agency or organization)Lehigh Fed: $132,876.68
Lehigh Match: $133,264.60
Total Project Cost$266,141.28
Start and End Dates01/20/2020 - 07/31/2022
Brief Description of Research ProjectMobile sensing is an alternative paradigm which offers numerous
advantages compared to the conventional stationary sensor
networks. Mobile sensors have low setup costs, collect spatial
information efficiently, and require no dedicated sensors to any
particular structure. Most importantly, they can capture
comprehensive spatial information using few sensors. The
advantages of mobile sensing combined with the ubiquity of
smartphones with the internet of things (IoT) connectivity have
motivated researchers to think of cars+smart phones as large-scale
sensor networks that can contribute to the health assessment of
structures. Working with mobile sensors has several challenges.
The signals collected within vehicles’ cabin is contaminated by the
vehicle suspension dynamics; therefore, the extraction of bridge
vibration from signals collected within a vehicle is not an easy task.
Additionally, mobile sensors simultaneously measure vibration
data in time while scanning over a large set of points in space which
creates a different data structure compared with fixed sensors. Since
collected data is mixed in time and space, it contains spatial
discontinuities. When these challenges are addressed, mobile
sensing is a promising data resource enabling crowdsourcing and an
opportunity to extract information about infrastructure conditions at
an unprecedented rate and resolution. This project proposes a deep
learning framework specific to mobile sensing to perform system
identification and life-cycle assessment of bridge structures.