Digital Twin Framework for Bridge Structural Health Monitoring Utilizing Existing Technologies: New Paradigm for Enhanced Management, Operation, and Maintenance

Abstract

Bridges are one of the most important elements of a sustainable transportation infrastructure network and require significant care to guarantee they function in a safe and reliable manner. Ensuring effective bridge maintenance and life cycle management in the face of aging and deterioration requires precise performance data and efficient assessment technologies. Because of the overall mediocre quality of U.S. infrastructure, there is a crucial need to enhance its monitoring and management. Digital twin is an emerging and promising concept that can offer the required solution. Advances in sensors, computing, communications, and predictive data analytics, coupled with biannual inspections, offer a wealth of bridge assessment data that will need a centralized digital twin framework for integration, analysis, and optimal decision-making with regard to maintenance. Leveraging and integrating these technologies in the new paradigm of a bridge digital twin will provide state transportation agencies with additional resources not previously available to enhance bridge operation and maintenance. A major value of a digital twin is the new knowledge created by the integration of information resources. This paper provides a framework for bridge digital twin that incorporates existing technologies, namely, building information modeling, structural health monitoring, intelligent transportation systems, and geographic information systems. The paper also provides an overview of practical applications and use cases of digital twin for bridges and structures, and offers an assessment of the challenges posed by the technology.