The HBIM-GNN Integration Strategy
1. Introduction to the Challenge
Historic structures, unlike modern buildings, possess complex geometries and non-standard material properties. Traditional Finite Element Methods (FEM) often require excessive computational time (10+ hours) to simulate stress distribution in these structures.
Fig 1: Data Fusion Pipeline
Fig 2: GNN Node Classification
2. Our Solution: Graph Neural Networks
At Ivan Emtiaz Energy, we convert the HBIM into a semantic graph. Each masonry brick acts as a 'Node', and the mortar acts as an 'Edge'. By training our GNN on historical stress data, we can predict structural failure in milliseconds.
3. Real-time Visualization
The output is fed into a web-based Digital Twin dashboard. Stakeholders can see a color-coded 3D model instantly.
Fig 3: Live Monitoring Dashboard
Fig 4: IoT Sensor Placement