Developing an instrumentation architecture and a self-learning algorithm for creating digital twins of municipal bridges

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A digital twin is a virtual model that is an exact representation (or twin) of a physical system. Digital twin technology supports proactive infrastructure management by assisting in monitoring and maintaining, optimizing performance, and understanding failure cause. Digital twin technology increases the service life, safety, resiliency, and level of service of civil infrastructure while decreasing operational costs. Bridge infrastructure is a crucial component of civil infrastructure systems. However, the digital twin technology has not yet been used for bridge infrastructure management in Canada. The objective of this research is to develop the system architecture for creating a digital twin of a bridge infrastructure to aid dynamic infrastructure management decision-making. This research aims to develop an instrumentation architecture for real-time monitoring and acquisition of the physical infrastructure data. Moreover, a self-learning algorithm will be developed to predict bridge deterioration. This research proposes a state-of-the-art solution for current bridge infrastructure management challenges. The outcomes of this research will support United Nations Sustainable Development Goals, sustainable cities and communities (Goal # 11), and industry innovation and infrastructure (Goal # 9).