Advanced Infrastructure Monitoring: Leveraging Simulation, IoT, and Machine Learning Toward Resilient Society

Advances in simulation, Internet of Things (IoT), and machine learning technologies have revolutionized the field of infrastructure monitoring, offering new possibilities for enhancing the resilience and sustainability of societal systems. This keynote lecture will explore cutting-edge developments in monitoring methodologies applied to roads, railways, and civil structures. Two primary approaches will be highlighted: sensor-based fixed monitoring and probe-based mobile monitoring.

Fixed monitoring involves the strategic placement of sensors on infrastructure to enable observation of performance and condition. In contrast, mobile monitoring leverages sensor-equipped probe vehicles, which collect data dynamically as they traverse infrastructure networks, offering broader spatial coverage and flexibility. By integrating simulation models, IoT-enabled devices, and machine learning algorithms, these methods facilitate more accurate assessments of infrastructure health, early detection of anomalies, and informed decision-making for maintenance and management.

Through case studies and recent applications, this presentation will illustrate how these technologies converge to address challenges in monitoring complex systems, supporting the vision of a resilient society. The discussion will also consider the potential for future innovation and collaboration across academia, industry, and government to further advance infrastructure monitoring.