3D MEP Component Predictor
Neural network system for automated MEP placement in BIM models
>50%
Resource Savings
3D + Type
Prediction
MLP + CNN
Models
BIM/Revit
Domain
Problem
Placing MEP (Mechanical, Electrical, Plumbing) components in 3D building models is a time-consuming manual process requiring specialized knowledge. Equans engineers spend significant hours on repetitive placement tasks.
Approach
Developed PyTorch neural networks (MLP and 1D CNN with depthwise separable convolutions and residual blocks) that predict both the 3D coordinates (x, y, z) and component type for MEP elements within Revit building models.
Data Extraction
BIM model parsing
- Revit IFC export
- 3D coordinate extraction
- Component type labeling
Feature Engineering
Spatial encoding
- Room context features
- Adjacency matrices
- Normalized coordinates
Model Training
Neural network
- MLP baseline
- 1D CNN with residual blocks
- Depthwise separable convolutions
Deployment
Revit integration
- Coordinate prediction
- Component type classification
- Batch placement
Results
Achieved over 50% reduction in resource requirements for MEP placement tasks. The model accurately predicts component locations and types, significantly accelerating the BIM workflow.