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BIM Quantity Takeoff\r

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Overview\r

Quantity Takeoff (QTO) extracts measurable quantities from BIM models. This skill processes BIM exports to generate grouped quantity reports for cost estimation.\r \r

Python Implementation\r

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import pandas as pd\r
import numpy as np\r
from typing import Dict, Any, List, Optional, Tuple\r
from dataclasses import dataclass, field\r
from enum import Enum\r
\r
\r
class QTOUnit(Enum):\r
    """Quantity takeoff measurement units."""\r
    COUNT = "ea"\r
    LENGTH = "m"\r
    AREA = "m2"\r
    VOLUME = "m3"\r
    WEIGHT = "kg"\r
    LINEAR_FOOT = "lf"\r
    SQUARE_FOOT = "sf"\r
    CUBIC_YARD = "cy"\r
\r
\r
@dataclass\r
class QTOItem:\r
    """Single QTO line item."""\r
    category: str\r
    type_name: str\r
    description: str\r
    quantity: float\r
    unit: str\r
    level: Optional[str] = None\r
    material: Optional[str] = None\r
    element_count: int = 0\r
\r
\r
@dataclass\r
class QTOReport:\r
    """Complete QTO report."""\r
    project_name: str\r
    items: List[QTOItem]\r
    total_elements: int\r
    categories: int\r
    generated_date: str\r
\r
\r
class BIMQuantityTakeoff:\r
    """Extract quantities from BIM data."""\r
\r
    # Column mappings for different BIM exports\r
    COLUMN_MAPPINGS = {\r
        'type': ['Type Name', 'TypeName', 'type_name', 'Family and Type', 'IfcType'],\r
        'category': ['Category', 'category', 'IfcClass', 'Element Category'],\r
        'level': ['Level', 'level', 'Building Storey', 'BuildingStorey', 'Floor'],\r
        'volume': ['Volume', 'volume', 'Volume (m³)', 'Qty_Volume'],\r
        'area': ['Area', 'area', 'Surface Area', 'Area (m²)', 'Qty_Area'],\r
        'length': ['Length', 'length', 'Length (m)', 'Qty_Length'],\r
        'count': ['Count', 'count', 'Quantity', 'ElementCount'],\r
        'material': ['Material', 'material', 'Structural Material', 'MaterialName']\r
    }\r
\r
    def __init__(self, df: pd.DataFrame):\r
        """Initialize with BIM data DataFrame."""\r
        self.df = df\r
        self.column_map = self._detect_columns()\r
\r
    def _detect_columns(self) -> Dict[str, str]:\r
        """Detect which columns exist in data."""\r
        mapping = {}\r
\r
        for standard, variants in self.COLUMN_MAPPINGS.items():\r
            for variant in variants:\r
                if variant in self.df.columns:\r
                    mapping[standard] = variant\r
                    break\r
\r
        return mapping\r
\r
    def get_column(self, standard_name: str) -> Optional[str]:\r
        """Get actual column name from standard name."""\r
        return self.column_map.get(standard_name)\r
\r
    def group_by_type(self, sum_column: str = 'volume') -> pd.DataFrame:\r
        """Group quantities by type name."""\r
\r
        type_col = self.get_column('type')\r
        qty_col = self.get_column(sum_column)\r
\r
        if type_col is None:\r
            raise ValueError("Type column not found")\r
\r
        if qty_col is None:\r
            # Fall back to count\r
            result = self.df.groupby(type_col).size().reset_index(name='count')\r
        else:\r
            result = self.df.groupby(type_col).agg({\r
                qty_col: 'sum'\r
            }).reset_index()\r
            result['count'] = self.df.groupby(type_col).size().values\r
\r
        result.columns = ['Type', 'Quantity', 'Count'] if len(result.columns) == 3 else ['Type', 'Count']\r
        return result.sort_values('Count', ascending=False)\r
\r
    def group_by_category(self, sum_column: str = 'volume') -> pd.DataFrame:\r
        """Group quantities by category."""\r
\r
        cat_col = self.get_column('category')\r
        qty_col = self.get_column(sum_column)\r
\r
        if cat_col is None:\r
            raise ValueError("Category column not found")\r
\r
        agg_dict = {}\r
        if qty_col:\r
            agg_dict[qty_col] = 'sum'\r
\r
        if agg_dict:\r
            result = self.df.groupby(cat_col).agg(agg_dict).reset_index()\r
            result['count'] = self.df.groupby(cat_col).size().values\r
        else:\r
            result = self.df.groupby(cat_col).size().reset_index(name='count')\r
\r
        return result.sort_values('count', ascending=False)\r
\r
    def group_by_level(self, sum_column: str = 'volume') -> pd.DataFrame:\r
        """Group quantities by building level."""\r
\r
        level_col = self.get_column('level')\r
        qty_col = self.get_column(sum_column)\r
\r
        if level_col is None:\r
            raise ValueError("Level column not found")\r
\r
        agg_dict = {}\r
        if qty_col:\r
            agg_dict[qty_col] = 'sum'\r
\r
        if agg_dict:\r
            result = self.df.groupby(level_col).agg(agg_dict).reset_index()\r
            result['count'] = self.df.groupby(level_col).size().values\r
        else:\r
            result = self.df.groupby(level_col).size().reset_index(name='count')\r
\r
        return result\r
\r
    def pivot_by_level_and_type(self) -> pd.DataFrame:\r
        """Create pivot table: levels as rows, types as columns."""\r
\r
        level_col = self.get_column('level')\r
        type_col = self.get_column('type')\r
\r
        if level_col is None or type_col is None:\r
            raise ValueError("Level or Type column not found")\r
\r
        pivot = pd.crosstab(\r
            self.df[level_col],\r
            self.df[type_col],\r
            margins=True\r
        )\r
\r
        return pivot\r
\r
    def filter_by_category(self, categories: List[str]) -> 'BIMQuantityTakeoff':\r
        """Filter to specific categories."""\r
\r
        cat_col = self.get_column('category')\r
        if cat_col is None:\r
            raise ValueError("Category column not found")\r
\r
        filtered_df = self.df[self.df[cat_col].isin(categories)]\r
        return BIMQuantityTakeoff(filtered_df)\r
\r
    def filter_by_level(self, levels: List[str]) -> 'BIMQuantityTakeoff':\r
        """Filter to specific levels."""\r
\r
        level_col = self.get_column('level')\r
        if level_col is None:\r
            raise ValueError("Level column not found")\r
\r
        filtered_df = self.df[self.df[level_col].isin(levels)]\r
        return BIMQuantityTakeoff(filtered_df)\r
\r
    def get_walls(self) -> pd.DataFrame:\r
        """Get wall quantities."""\r
        cat_col = self.get_column('category')\r
        if cat_col:\r
            walls = self.df[self.df[cat_col].str.contains('Wall', case=False, na=False)]\r
            return BIMQuantityTakeoff(walls).group_by_type()\r
        return pd.DataFrame()\r
\r
    def get_floors(self) -> pd.DataFrame:\r
        """Get floor/slab quantities."""\r
        cat_col = self.get_column('category')\r
        if cat_col:\r
            floors = self.df[self.df[cat_col].str.contains('Floor|Slab', case=False, na=False)]\r
            return BIMQuantityTakeoff(floors).group_by_type()\r
        return pd.DataFrame()\r
\r
    def get_doors(self) -> pd.DataFrame:\r
        """Get door quantities."""\r
        cat_col = self.get_column('category')\r
        if cat_col:\r
            doors = self.df[self.df[cat_col].str.contains('Door', case=False, na=False)]\r
            return BIMQuantityTakeoff(doors).group_by_type()\r
        return pd.DataFrame()\r
\r
    def get_windows(self) -> pd.DataFrame:\r
        """Get window quantities."""\r
        cat_col = self.get_column('category')\r
        if cat_col:\r
            windows = self.df[self.df[cat_col].str.contains('Window', case=False, na=False)]\r
            return BIMQuantityTakeoff(windows).group_by_type()\r
        return pd.DataFrame()\r
\r
    def generate_report(self, project_name: str = "Project") -> QTOReport:\r
        """Generate complete QTO report."""\r
\r
        from datetime import datetime\r
\r
        items = []\r
        type_col = self.get_column('type')\r
        cat_col = self.get_column('category')\r
        level_col = self.get_column('level')\r
        vol_col = self.get_column('volume')\r
        area_col = self.get_column('area')\r
        mat_col = self.get_column('material')\r
\r
        # Group by type\r
        grouped = self.df.groupby(type_col if type_col else self.df.columns[0])\r
\r
        for type_name, group in grouped:\r
            # Determine primary quantity\r
            qty = 0\r
            unit = QTOUnit.COUNT.value\r
\r
            if vol_col and vol_col in group.columns:\r
                qty = group[vol_col].sum()\r
                unit = QTOUnit.VOLUME.value\r
            elif area_col and area_col in group.columns:\r
                qty = group[area_col].sum()\r
                unit = QTOUnit.AREA.value\r
            else:\r
                qty = len(group)\r
                unit = QTOUnit.COUNT.value\r
\r
            # Get category and material\r
            category = group[cat_col].iloc[0] if cat_col and cat_col in group.columns else ""\r
            material = group[mat_col].iloc[0] if mat_col and mat_col in group.columns else ""\r
            level = group[level_col].iloc[0] if level_col and level_col in group.columns else ""\r
\r
            items.append(QTOItem(\r
                category=str(category),\r
                type_name=str(type_name),\r
                description=str(type_name),\r
                quantity=round(qty, 2),\r
                unit=unit,\r
                level=str(level) if level else None,\r
                material=str(material) if material else None,\r
                element_count=len(group)\r
            ))\r
\r
        return QTOReport(\r
            project_name=project_name,\r
            items=items,\r
            total_elements=len(self.df),\r
            categories=self.df[cat_col].nunique() if cat_col else 0,\r
            generated_date=datetime.now().isoformat()\r
        )\r
\r
    def to_excel(self, output_path: str, project_name: str = "Project"):\r
        """Export QTO to Excel with multiple sheets."""\r
\r
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:\r
            # Summary by category\r
            self.group_by_category().to_excel(\r
                writer, sheet_name='By Category', index=False)\r
\r
            # Summary by type\r
            self.group_by_type().to_excel(\r
                writer, sheet_name='By Type', index=False)\r
\r
            # Level breakdown\r
            try:\r
                self.pivot_by_level_and_type().to_excel(\r
                    writer, sheet_name='Level-Type Matrix')\r
            except:\r
                pass\r
\r
            # Walls\r
            walls = self.get_walls()\r
            if not walls.empty:\r
                walls.to_excel(writer, sheet_name='Walls', index=False)\r
\r
            # Doors and Windows\r
            doors = self.get_doors()\r
            if not doors.empty:\r
                doors.to_excel(writer, sheet_name='Doors', index=False)\r
\r
            windows = self.get_windows()\r
            if not windows.empty:\r
                windows.to_excel(writer, sheet_name='Windows', index=False)\r
\r
        return output_path\r
```\r
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## Quick Start\r
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```python\r
# Load BIM export\r
df = pd.read_excel("revit_export.xlsx")\r
\r
# Initialize QTO\r
qto = BIMQuantityTakeoff(df)\r
\r
# Get quantities by type\r
by_type = qto.group_by_type()\r
print(by_type.head(10))\r
\r
# Get wall schedule\r
walls = qto.get_walls()\r
print(walls)\r
```\r
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## Common Use Cases\r
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### 1. Full QTO Report\r
```python\r
qto = BIMQuantityTakeoff(df)\r
report = qto.generate_report("Office Building")\r
print(f"Elements: {report.total_elements}")\r
for item in report.items[:5]:\r
    print(f"{item.type_name}: {item.quantity} {item.unit}")\r
```\r
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### 2. Level-by-Level Analysis\r
```python\r
pivot = qto.pivot_by_level_and_type()\r
print(pivot)\r
```\r
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### 3. Export to Excel\r
```python\r
qto.to_excel("qto_report.xlsx", "My Project")\r
```\r
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## Resources\r
- **DDC Book**: Chapter 3.2 - Quantity Take-Off\r