Description
Intelligent models are often underused after design, remaining static and disconnected from real-world operations. This session will present a real-world deployment in a metropolitan rail system, where BIM is transformed into a live, AI-powered predictive maintenance platform. Using IFC-based workflows derived from Revit software, models are converted into lightweight, web-ready formats and integrated with Internet of Things (IoT) sensor data at the asset level. An AI layer enables anomaly detection and failure prediction, reducing unplanned downtime and improving maintenance efficiency. You will gain a practical understanding of how to extend BIM into an operational digital twin and implement scalable, data-driven maintenance strategies.
Key Learnings
- Learn how to build IFC-based workflows to transform intelligent models into real-time digital twins for operational use.
- Learn how to integrate IoT sensor data with asset-level BIM components for real-time monitoring and analysis.
- Learn about applying AI-driven methods to detect anomalies and predict equipment failures in infrastructure systems.
- Learn about designing scalable architectures for AI-powered digital twin systems in real-world infrastructure projects.