Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting.
apm install @datadrivenconstruction/ifc-qto-extraction
[](https://apm-p1ls2dz87-atlamors-projects.vercel.app/packages/@datadrivenconstruction/ifc-qto-extraction)---
name: "ifc-qto-extraction"
description: "Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw":{"emoji":"📐","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"],"anyBins":["IfcConvert","ifcopenshell"]}}}
---
# IFC Quantity Takeoff Extraction
Extract structured quantity data from BIM models (IFC, Revit) for cost estimation, material ordering, and progress tracking.
## Business Case
**Problem**: Manual quantity takeoff is:
- Time-consuming (40-80 hours for medium project)
- Error-prone (human counting mistakes)
- Not repeatable (changes require full rework)
- Disconnected from design (no live updates)
**Solution**: Automated QTO from BIM that:
- Extracts all quantities in minutes
- Groups by type, level, zone
- Updates instantly with model changes
- Exports to Excel for pricing
**ROI**: 90% reduction in QTO time, near-zero counting errors
## DDC Tools Used
```
┌──────────────────────────────────────────────────────────────────────┐
│ QTO EXTRACTION PIPELINE │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ INPUT CONVERT ANALYZE │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ .rvt │ │ DDC │ │ Python │ │
│ │ .ifc │─────────►│Converter│───────────►│ pandas │ │
│ │ .dwg │ │ │ │ │ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ │
│ │ .xlsx │ │ Grouped │ │
│ │ raw data│ │ QTO │ │
│ └─────────┘ └─────────┘ │
│ │ │
│ OUTPUT ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ QTO Report │ │
│ │ • Element counts by type │ │
│ │ • Areas (m², ft²) │ │
│ │ • Volumes (m³, ft³) │ │
│ │ • Lengths (m, ft) │ │
│ │ • Weights (kg, tons) │ │
│ │ • Grouped by level/zone/system │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────┘
```
## CLI Commands
### Revit to Excel (with BBox for volumes)
```bash
# Basic extraction
RvtExporter.exe "C:\Models\Building.rvt"
# Full extraction with bounding boxes (for volume calculations)
RvtExporter.exe "C:\Models\Building.rvt" complete bbox
# Include schedules (Revit's built-in QTO)
RvtExporter.exe "C:\Models\Building.rvt" complete bbox schedule
```
### IFC to Excel
```bash
# Extract IFC data
IfcExporter.exe "C:\Models\Building.ifc"
# Output: Building.xlsx with all IFC entities
```
### DWG to Excel (2D areas)
```bash
# Extract DWG blocks and areas
DwgExporter.exe "C:\Drawings\FloorPlan.dwg"
```
## Python Implementation
```python
import pandas as pd
import numpy as np
from pathlib import Path
import subprocess
from typing import List, Dict, Optional
from dataclasses import dataclass
@dataclass
class QuantityItem:
"""Single quantity line item"""
category: str
type_name: str
count: int
area: float = 0.0
volume: float = 0.0
length: float = 0.0
weight: float = 0.0
unit_area: str = "m²"
unit_volume: str = "m³"
unit_length: str = "m"
level: str = ""
zone: str = ""
class BIMQuantityExtractor:
"""Extract quantities from BIM models using DDC converters"""
def __init__(self, converter_path: str):
self.converter_path = Path(converter_path)
def convert_model(self, model_path: str, options: List[str] = None) -> Path:
"""Convert BIM model to Excel"""
model = Path(model_path)
options = options or ["complete", "bbox"]
# Determine converter
ext = model.suffix.lower()
converters = {
'.rvt': 'RvtExporter.exe',
'.rfa': 'RvtExporter.exe',
'.ifc': 'IfcExporter.exe',
'.dwg': 'DwgExporter.exe',
'.dgn': 'DgnExporter.exe'
}
converter = self.converter_path / converters.get(ext, 'RvtExporter.exe')
# Build command
cmd = [str(converter), str(model)] + options
# Execute
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"Conversion failed: {result.stderr}")
# Return path to generated Excel
xlsx_path = model.with_suffix('.xlsx')
return xlsx_path
def load_bim_data(self, xlsx_path: str) -> pd.DataFrame:
"""Load converted BIM data from Excel"""
xlsx = Path(xlsx_path)
if not xlsx.exists():
raise FileNotFoundError(f"Excel file not found: {xlsx}")
# Read main data sheet
df = pd.read_excel(xlsx, sheet_name=0)
# Clean column names
df.columns = df.columns.str.strip()
return df
def extract_quantities(
self,
df: pd.DataFrame,
group_by: str = "Type Name",
include_categories: List[str] = None
) -> List[QuantityItem]:
"""Extract quantities grouped by type"""
# Filter categories if specified
if include_categories and 'Category' in df.columns:
df = df[df['Category'].isin(include_categories)]
# Group and aggregate
quantities = []
for (category, type_name), group in df.groupby(['Category', group_by]):
item = QuantityItem(
category=str(category),
type_name=str(type_name),
count=len(group)
)
# Extract area
area_cols = ['Area', 'Surface Area', 'Gross Area', 'Net Area']
for col in area_cols:
if col in group.columns:
item.area = group[col].sum()
break
# Extract volume
vol_cols = ['Volume', 'Gross Volume', 'Net Volume']
for col in vol_cols:
if col in group.columns:
item.volume = group[col].sum()
break
# Extract length
len_cols = ['Length', 'Curve Length', 'Unconnected Height']
for col in len_cols:
if col in group.columns:
item.length = group[col].sum()
break
# Extract level if available
if 'Level' in group.columns:
levels = group['Level'].dropna().unique()
item.level = ', '.join(str(l) for l in levels)
quantities.append(item)
return quantities
def extract_by_level(
self,
df: pd.DataFrame,
group_by: str = "Type Name"
) -> Dict[str, List[QuantityItem]]:
"""Extract quantities grouped by level"""
result = {}
if 'Level' not in df.columns:
result['All Levels'] = self.extract_quantities(df, group_by)
return result
for level, level_df in df.groupby('Level'):
level_name = str(level) if pd.notna(level) else 'Unassigned'
result[level_name] = self.extract_quantities(level_df, group_by)
return result
def calculate_concrete_quantities(self, df: pd.DataFrame) -> dict:
"""Calculate concrete quantities for typical elements"""
concrete_categories = [
'Floors', 'Structural Floors',
'Walls', 'Structural Walls',
'Structural Foundations', 'Foundation',
'Structural Columns', 'Columns',
'Structural Framing', 'Beams'
]
concrete_df = df[df['Category'].isin(concrete_categories)]
return {
'total_volume_m3': concrete_df['Volume'].sum() if 'Volume' in concrete_df.columns else 0,
'by_category': concrete_df.groupby('Category')['Volume'].sum().to_dict() if 'Volume' in concrete_df.columns else {},
'element_count': len(concrete_df)
}
def calculate_wall_quantities(self, df: pd.DataFrame) -> dict:
"""Calculate wall quantities"""
wall_categories = ['Walls', 'Basic Wall', 'Curtain Wall']
walls = df[df['Category'].isin(wall_categories)]
result = {
'total_area_m2': 0,
'total_length_m': 0,
'by_type': {}
}
if 'Area' in walls.columns:
result['total_area_m2'] = walls['Area'].sum()
if 'Length' in walls.columns:
result['total_length_m'] = walls['Length'].sum()
if 'Type Name' in walls.columns:
for type_name, group in walls.groupby('Type Name'):
result['by_type'][type_name] = {
'count': len(group),
'area': group['Area'].sum() if 'Area' in group.columns else 0,
'length': group['Length'].sum() if 'Length' in group.columns else 0
}
return result
def generate_qto_report(
self,
quantities: List[QuantityItem],
output_path: str,
project_name: str = "Project"
) -> str:
"""Generate QTO Excel report"""
# Convert to DataFrame
records = []
for q in quantities:
records.append({
'Category': q.category,
'Type': q.type_name,
'Count': q.count,
'Area (m²)': round(q.area, 2),
'Volume (m³)': round(q.volume, 3),
'Length (m)': round(q.length, 2),
'Level': q.level
})
df = pd.DataFrame(records)
# Sort by category and type
df = df.sort_values(['Category', 'Type'])
# Write to Excel with formatting
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary sheet
summary = df.groupby('Category').agg({
'Count': 'sum',
'Area (m²)': 'sum',
'Volume (m³)': 'sum',
'Length (m)': 'sum'
}).round(2)
summary.to_excel(writer, sheet_name='Summary')
# Detail sheet
df.to_excel(writer, sheet_name='Detail', index=False)
# By Level sheet
if 'Level' in df.columns and df['Level'].notna().any():
level_summary = df.groupby(['Level', 'Category']).agg({
'Count': 'sum',
'Area (m²)': 'sum',
'Volume (m³)': 'sum'
}).round(2)
level_summary.to_excel(writer, sheet_name='By Level')
return output_path
def generate_html_report(
self,
quantities: List[QuantityItem],
output_path: str,
project_name: str = "Project"
) -> str:
"""Generate interactive HTML QTO report"""
# Group by category
by_category = {}
for q in quantities:
if q.category not in by_category:
by_category[q.category] = []
by_category[q.category].append(q)
# Calculate totals
total_count = sum(q.count for q in quantities)
total_area = sum(q.area for q in quantities)
total_volume = sum(q.volume for q in quantities)
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>QTO Report - {project_name}</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 20px; }}
.header {{ background: #2c3e50; color: white; padding: 20px; margin-bottom: 20px; }}
.summary {{ display: flex; gap: 20px; margin-bottom: 20px; }}
.summary-card {{ background: #ecf0f1; padding: 15px; border-radius: 5px; flex: 1; }}
.summary-card h3 {{ margin: 0 0 10px 0; color: #7f8c8d; font-size: 14px; }}
.summary-card .value {{ font-size: 24px; font-weight: bold; color: #2c3e50; }}
table {{ width: 100%; border-collapse: collapse; margin-bottom: 20px; }}
th {{ background: #34495e; color: white; padding: 10px; text-align: left; }}
td {{ padding: 8px; border-bottom: 1px solid #ddd; }}
tr:hover {{ background: #f5f5f5; }}
.category-header {{ background: #3498db; color: white; font-weight: bold; }}
.number {{ text-align: right; }}
</style>
</head>
<body>
<div class="header">
<h1>Quantity Takeoff Report</h1>
<p>Project: {project_name}</p>
</div>
<div class="summary">
<div class="summary-card">
<h3>Total Elements</h3>
<div class="value">{total_count:,}</div>
</div>
<div class="summary-card">
<h3>Total Area</h3>
<div class="value">{total_area:,.2f} m²</div>
</div>
<div class="summary-card">
<h3>Total Volume</h3>
<div class="value">{total_volume:,.3f} m³</div>
</div>
<div class="summary-card">
<h3>Categories</h3>
<div class="value">{len(by_category)}</div>
</div>
</div>
<table>
<thead>
<tr>
<th>Category / Type</th>
<th class="number">Count</th>
<th class="number">Area (m²)</th>
<th class="number">Volume (m³)</th>
<th class="number">Length (m)</th>
</tr>
</thead>
<tbody>
"""
for category, items in sorted(by_category.items()):
cat_count = sum(i.count for i in items)
cat_area = sum(i.area for i in items)
cat_volume = sum(i.volume for i in items)
html += f"""
<tr class="category-header">
<td>{category}</td>
<td class="number">{cat_count:,}</td>
<td class="number">{cat_area:,.2f}</td>
<td class="number">{cat_volume:,.3f}</td>
<td class="number">-</td>
</tr>
"""
for item in sorted(items, key=lambda x: x.type_name):
html += f"""
<tr>
<td> {item.type_name}</td>
<td class="number">{item.count:,}</td>
<td class="number">{item.area:,.2f}</td>
<td class="number">{item.volume:,.3f}</td>
<td class="number">{item.length:,.2f}</td>
</tr>
"""
html += """
</tbody>
</table>
</body>
</html>
"""
with open(output_path, 'w', encoding='utf-8') as f:
f.write(html)
return output_path
# Usage Example
def extract_qto_from_model(
model_path: str,
converter_path: str,
output_dir: str = None
) -> dict:
"""Complete QTO extraction workflow"""
from datetime import datetime
extractor = BIMQuantityExtractor(converter_path)
# Convert model
print(f"Converting: {model_path}")
xlsx_path = extractor.convert_model(model_path, ["complete", "bbox"])
# Load data
print(f"Loading data from: {xlsx_path}")
df = extractor.load_bim_data(xlsx_path)
# Extract quantities
quantities = extractor.extract_quantities(df)
# Generate reports
output_dir = output_dir or Path(model_path).parent
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
excel_path = Path(output_dir) / f"QTO_{timestamp}.xlsx"
html_path = Path(output_dir) / f"QTO_{timestamp}.html"
extractor.generate_qto_report(quantities, str(excel_path))
extractor.generate_html_report(quantities, str(html_path))
# Calculate specific quantities
concrete = extractor.calculate_concrete_quantities(df)
walls = extractor.calculate_wall_quantities(df)
return {
'excel_report': str(excel_path),
'html_report': str(html_path),
'summary': {
'total_elements': len(df),
'categories': df['Category'].nunique() if 'Category' in df.columns else 0,
'types': df['Type Name'].nunique() if 'Type Name' in df.columns else 0
},
'concrete': concrete,
'walls': walls
}
if __name__ == "__main__":
result = extract_qto_from_model(
model_path=r"C:\Projects\Building.rvt",
converter_path=r"C:\DDC\Converters",
output_dir=r"C:\Projects\QTO"
)
print(f"Excel: {result['excel_report']}")
print(f"HTML: {result['html_report']}")
print(f"Concrete Volume: {result['concrete']['total_volume_m3']:.2f} m³")
```
## n8n Workflow Integration
```yaml
name: BIM QTO Extraction
trigger:
type: webhook
path: /qto-extract
steps:
- convert_model:
node: Execute Command
command: |
"C:\DDC\RvtExporter.exe" "{{$json.model_path}}" complete bbox schedule
- load_excel:
node: Spreadsheet File
operation: read
file: "={{$json.model_path.replace('.rvt', '.xlsx')}}"
- group_quantities:
node: Code
code: |
const grouped = {};
items.forEach(item => {
const type = item.json['Type Name'];
if (!grouped[type]) {
grouped[type] = {
count: 0,
area: 0,
volume: 0
};
}
grouped[type].count++;
grouped[type].area += parseFloat(item.json['Area'] || 0);
grouped[type].volume += parseFloat(item.json['Volume'] || 0);
});
return Object.entries(grouped).map(([type, data]) => ({
type,
...data
}));
- generate_report:
node: Code
code: |
// Generate HTML report
return generateHTMLReport(items);
- save_report:
node: Write Binary File
path: "={{$json.output_path}}"
```
## Best Practices
1. **Model Quality**: Ensure BIM model has proper levels and types assigned
2. **Units**: Verify model units match expected output units
3. **Categories**: Use consistent category naming for grouping
4. **Updates**: Re-run QTO after design changes
5. **Validation**: Cross-check totals against manual spot checks
## Common Quantity Formulas
```python
# Concrete formwork area (approximate)
formwork_area = concrete_volume * 6 # m² per m³ of concrete
# Rebar quantity (approximate)
rebar_weight = concrete_volume * 100 # kg per m³ (typical)
# Paint area from wall area
paint_area = wall_area * 2 # both sides
# Ceiling area from floor area
ceiling_area = floor_area * 0.95 # typical ratio
```
---
*"Measure twice, cut once. Or better yet, measure automatically from the model."*