Artificial Intelligence in Infrastructure Monitoring & Asset Management
 

Course Description:

This course provides Professional Engineers with a comprehensive, cross-disciplinary framework for understanding and applying AI in infrastructure monitoring and asset management. The course covers foundational AI concepts, sensor networks and data quality, sensor fusion and anomaly detection, predictive versus condition-based maintenance strategies, engineering interpretation of AI alerts, risk and reliability considerations, and integration of AI tools into formal asset management programs.

Learning Objectives:

Upon completion of this course, the learner will be able to:

1. Explain how AI and machine learning are used in infrastructure monitoring and asset management systems.
2. Identify common infrastructure applications for AI, including bridges, pavements, pipelines, and electrical grids.
3. Describe how sensor data is collected, integrated, and analyzed using AI techniques.
4. Differentiate between predictive maintenance and condition-based maintenance approaches.
5. Recognize common sources of error, uncertainty, and bias in AI-based monitoring systems.
6. Interpret AI-generated alerts using sound engineering judgment and contextual knowledge.
7. Evaluate risks associated with false positives and false negatives in critical infrastructure monitoring.
8. Apply lessons learned from real-world case studies involving AI-based asset management.
9. Integrate AI tools into existing inspection, maintenance, and asset management workflows responsibly.
10. Maintain professional responsibility and accountability when relying on AI-assisted infrastructure monitoring.