Structural health monitoring of the Confederation Bridge: From innovation to engineering practices

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Structural health monitoring (SHM) has become the subject of intense research in recent years because of the increasing need for efficient and cost-effective management of infrastructure, and assessment of their reliability. As part of this global research effort, researchers at Carleton University and the University of Calgary have carried out a large-scale university-government-industry research project on the long-term monitoring of the performance of the Confederation Bridge since its opening in 1997. Very large amounts of data have been systematically collected by a sophisticated data acquisition system in order to assess the performance of the structure. The main goal of the monitoring project is to obtain information on the health and/or condition of the bridge by intelligent processing of the monitoring data so as to detect unexpected deterioration of the structure that may reduce the design reliability of the structure, and/or its service life.     The Safety and Security of Canada's infrastructure is integral to our National Security Policy.  As such, the assessment of infrastructure components for their risk and reliability, and having the technological capabilities to detect the effects of age and use on their risk and reliability,  are the major elements and fundamental to the formulation of such a security policy.  This proposal addresses the development of tools that strengthen Canadais ability to assess the risk and reliability of its national infrastructure, and research on cutting edge numerical techniques to detect events or phenomena that can have a serious impact on the safety and security of critical infrastructure.  This proposal also addresses the issue of the management of huge amounts of data in a Distributed Networked Environment for the real-time assessment of structural performance and reliability.  The proposed research serves as a test-bed project to develop innovative numerical techniques and algorithms to advance the field of structural health monitoirng from innovations to engineering practice in the field.