BIM Quantity Takeoff
Overview
Quantity Takeoff (QTO) extracts measurable quantities from BIM models. This skill processes BIM exports to generate grouped quantity reports for cost estimation.
Python Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
class QTOUnit(Enum):
"""Quantity takeoff measurement units."""
COUNT = "ea"
LENGTH = "m"
AREA = "m2"
VOLUME = "m3"
WEIGHT = "kg"
LINEAR_FOOT = "lf"
SQUARE_FOOT = "sf"
CUBIC_YARD = "cy"
@dataclass
class QTOItem:
"""Single QTO line item."""
category: str
type_name: str
description: str
quantity: float
unit: str
level: Optional[str] = None
material: Optional[str] = None
element_count: int = 0
@dataclass
class QTOReport:
"""Complete QTO report."""
project_name: str
items: List[QTOItem]
total_elements: int
categories: int
generated_date: str
class BIMQuantityTakeoff:
"""Extract quantities from BIM data."""
# Column mappings for different BIM exports
COLUMN_MAPPINGS = {
'type': ['Type Name', 'TypeName', 'type_name', 'Family and Type', 'IfcType'],
'category': ['Category', 'category', 'IfcClass', 'Element Category'],
'level': ['Level', 'level', 'Building Storey', 'BuildingStorey', 'Floor'],
'volume': ['Volume', 'volume', 'Volume (m³)', 'Qty_Volume'],
'area': ['Area', 'area', 'Surface Area', 'Area (m²)', 'Qty_Area'],
'length': ['Length', 'length', 'Length (m)', 'Qty_Length'],
'count': ['Count', 'count', 'Quantity', 'ElementCount'],
'material': ['Material', 'material', 'Structural Material', 'MaterialName']
}
def __init__(self, df: pd.DataFrame):
"""Initialize with BIM data DataFrame."""
self.df = df
self.column_map = self._detect_columns()
def _detect_columns(self) -> Dict[str, str]:
"""Detect which columns exist in data."""
mapping = {}
for standard, variants in self.COLUMN_MAPPINGS.items():
for variant in variants:
if variant in self.df.columns:
mapping[standard] = variant
break
return mapping
def get_column(self, standard_name: str) -> Optional[str]:
"""Get actual column name from standard name."""
return self.column_map.get(standard_name)
def group_by_type(self, sum_column: str = 'volume') -> pd.DataFrame:
"""Group quantities by type name."""
type_col = self.get_column('type')
qty_col = self.get_column(sum_column)
if type_col is None:
raise ValueError("Type column not found")
if qty_col is None:
# Fall back to count
result = self.df.groupby(type_col).size().reset_index(name='count')
else:
result = self.df.groupby(type_col).agg({
qty_col: 'sum'
}).reset_index()
result['count'] = self.df.groupby(type_col).size().values
result.columns = ['Type', 'Quantity', 'Count'] if len(result.columns) == 3 else ['Type', 'Count']
return result.sort_values('Count', ascending=False)
def group_by_category(self, sum_column: str = 'volume') -> pd.DataFrame:
"""Group quantities by category."""
cat_col = self.get_column('category')
qty_col = self.get_column(sum_column)
if cat_col is None:
raise ValueError("Category column not found")
agg_dict = {}
if qty_col:
agg_dict[qty_col] = 'sum'
if agg_dict:
result = self.df.groupby(cat_col).agg(agg_dict).reset_index()
result['count'] = self.df.groupby(cat_col).size().values
else:
result = self.df.groupby(cat_col).size().reset_index(name='count')
return result.sort_values('count', ascending=False)
def group_by_level(self, sum_column: str = 'volume') -> pd.DataFrame:
"""Group quantities by building level."""
level_col = self.get_column('level')
qty_col = self.get_column(sum_column)
if level_col is None:
raise ValueError("Level column not found")
agg_dict = {}
if qty_col:
agg_dict[qty_col] = 'sum'
if agg_dict:
result = self.df.groupby(level_col).agg(agg_dict).reset_index()
result['count'] = self.df.groupby(level_col).size().values
else:
result = self.df.groupby(level_col).size().reset_index(name='count')
return result
def pivot_by_level_and_type(self) -> pd.DataFrame:
"""Create pivot table: levels as rows, types as columns."""
level_col = self.get_column('level')
type_col = self.get_column('type')
if level_col is None or type_col is None:
raise ValueError("Level or Type column not found")
pivot = pd.crosstab(
self.df[level_col],
self.df[type_col],
margins=True
)
return pivot
def filter_by_category(self, categories: List[str]) -> 'BIMQuantityTakeoff':
"""Filter to specific categories."""
cat_col = self.get_column('category')
if cat_col is None:
raise ValueError("Category column not found")
filtered_df = self.df[self.df[cat_col].isin(categories)]
return BIMQuantityTakeoff(filtered_df)
def filter_by_level(self, levels: List[str]) -> 'BIMQuantityTakeoff':
"""Filter to specific levels."""
level_col = self.get_column('level')
if level_col is None:
raise ValueError("Level column not found")
filtered_df = self.df[self.df[level_col].isin(levels)]
return BIMQuantityTakeoff(filtered_df)
def get_walls(self) -> pd.DataFrame:
"""Get wall quantities."""
cat_col = self.get_column('category')
if cat_col:
walls = self.df[self.df[cat_col].str.contains('Wall', case=False, na=False)]
return BIMQuantityTakeoff(walls).group_by_type()
return pd.DataFrame()
def get_floors(self) -> pd.DataFrame:
"""Get floor/slab quantities."""
cat_col = self.get_column('category')
if cat_col:
floors = self.df[self.df[cat_col].str.contains('Floor|Slab', case=False, na=False)]
return BIMQuantityTakeoff(floors).group_by_type()
return pd.DataFrame()
def get_doors(self) -> pd.DataFrame:
"""Get door quantities."""
cat_col = self.get_column('category')
if cat_col:
doors = self.df[self.df[cat_col].str.contains('Door', case=False, na=False)]
return BIMQuantityTakeoff(doors).group_by_type()
return pd.DataFrame()
def get_windows(self) -> pd.DataFrame:
"""Get window quantities."""
cat_col = self.get_column('category')
if cat_col:
windows = self.df[self.df[cat_col].str.contains('Window', case=False, na=False)]
return BIMQuantityTakeoff(windows).group_by_type()
return pd.DataFrame()
def generate_report(self, project_name: str = "Project") -> QTOReport:
"""Generate complete QTO report."""
from datetime import datetime
items = []
type_col = self.get_column('type')
cat_col = self.get_column('category')
level_col = self.get_column('level')
vol_col = self.get_column('volume')
area_col = self.get_column('area')
mat_col = self.get_column('material')
# Group by type
grouped = self.df.groupby(type_col if type_col else self.df.columns[0])
for type_name, group in grouped:
# Determine primary quantity
qty = 0
unit = QTOUnit.COUNT.value
if vol_col and vol_col in group.columns:
qty = group[vol_col].sum()
un