Towards a new generation of smart long-span bridges with auto-detection systems for nonlinear dynamic response and local deterioration under wind effect

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The main suspension bridges in Canada are approaching the end of their life span, as these were built during 1920-1940. The service of a bridge can be extended through maintenance, repairs and replacement of damaged structural elements. However, the complexity of these structures makes it difficult to decide which element must be repaired or replaced. Thus, the current Program will develop a Structural Intelligent (SI) Tool which will detect the damage in real-time when connected to the Structural Health Monitoring (SHM) system of each bridge, also indicating the potentially affected structural elements. The innovative SI Tool will be developed based on a vast damage and wind-induced fatigue database generated by HQP for training Machine Learning (ML) algorithms. The successful application of the SI Tool will be ensured through multiple calibration steps adjusted for suspension bridges of different lengths. Design and monitoring data necessary for this calibration are made available by Public Services and Procurement Canada (PSPC), who oversees the service of most of the suspension bridges in Canada. Once validated with SHM field data of a suspension bridge, the SI Tool will be able to predict future damage and wind-fatigue, when linked to any similar suspension bridge SHM system. A novel advance in knowledge is developing a new non-linear damage model for bridge structural elements, which will modify the frequency transfer functions to account for the non-linear damage reduction in damping and stiffness terms for each structural bridge element. The implementation will be done in Integrated Finite Strip Method (IFSM), where the PI has expertise, instead of FEM which relies on conventional geometric properties reduction, thus cannot realistically simulate damage. The short-term objectives of the proposed Research Program are to detect the critical damage of the aging Liard River Bridge, which connects indigenous communities, and to assist PSPC in monitoring and repairing/replacement decisions. The long-term objective is to implement a centralized auto-detection damage system which will allow PSPC and their partners to access remotely the structural condition of any instrumented and monitored suspension bridge and to decide towards repairing or replacement of the signaled damaged bridge structural elements. A diverse and inclusive team of 19 HQP (6PhD, 7MASc and 6UG) will enroll in this Program through selection criteria developed in collaboration with uOttawa offices of: Indigenous Affairs Office, Interdisciplinary Centre for Black Health and Women in Science and Engineering Committee, such that the privacy and the diversity of the HQP cohort are respected. The proposed Program will benefit of interdisciplinary co-supervisions with Dr. D. Inkpen, expert in ML algorithms and Dr. D. Lau, expert in SHM bridge data interpretation. Thus, the HQP will gain interdisciplinary technical skills and soft skills through participation in planned activities.