Combining Model Based and Data Based Techniques in a Robust Bridge Health Monitoring Algorithm

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CAIT project no.: CAIT-UTC-015

Fiscal Year: 2012/2013

Status: Final

Rutgers-CAIT Author(s): Raimondo Betti, Ph.D., Patrick Szary, Ph.D.

External Author(s): Dyab Khazem

Sponsor(s): Parsons Transportation Group, FHWA - RITA


This proposal focuses on the development of a robust methodology for the health monitoring of bridges using measurements of their structural response to ambient as well as earthquake and wind excitations. Since transportation infrastructure forms the backbone of the economic well-being and progress of any nation, proper maintenance and timely rehabilitation of bridges to ensure their uninterrupted functionality becomes a necessary pre-requisite in ensuring continued social and economic development in any country. While historically such inspection and maintenance operations involved visual, and thus, manual effort, the huge growth of the infrastructure systems and the continuous advancements in computer technology have paved the way for the development of automatic inspection techniques/technologies for civil infrastructure systems. One of these modern methodologies is vibration based structural health monitoring that uses information from the recorded motion of a structure to assess whether there is damage and its location. In the past such health monitoring exercises have involved either using the so called “model based” techniques, or using the “data based” pattern recognition techniques. However, these two different approaches should be treated as complementary for the development of a robust health monitoring and damage detection algorithm because, while both these approaches when considered individually have certain limitations, the limitations of one can be addressed through the advantages offered by the other.