CWICR Quantity Matcher
Business Case
Problem Statement
BIM exports contain quantities but:
- Element categories don't match cost codes
- Manual mapping is error-prone
- Different naming conventions
- Need consistent code assignment
Solution
Intelligent matching of BIM element quantities to CWICR work items using category mapping, semantic matching, and rule-based assignment.
Business Value
- Automation - Reduce manual mapping effort
- Consistency - Standard code assignment
- Accuracy - Validated quantity linkage
- Integration - BIM-to-cost data flow
Technical 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
import re
from difflib import SequenceMatcher
class MatchMethod(Enum):
"""Methods for matching BIM elements to work items."""
EXACT = "exact"
CATEGORY = "category"
SEMANTIC = "semantic"
RULE_BASED = "rule_based"
MANUAL = "manual"
class MatchConfidence(Enum):
"""Confidence level of match."""
HIGH = "high" # >90% confidence
MEDIUM = "medium" # 70-90%
LOW = "low" # 50-70%
MANUAL = "manual" # <50% - needs review
@dataclass
class QuantityMatch:
"""Single quantity match result."""
bim_element_id: str
bim_category: str
bim_description: str
bim_quantity: float
bim_unit: str
matched_work_item: str
work_item_description: str
work_item_unit: str
match_method: MatchMethod
confidence: MatchConfidence
confidence_score: float
unit_conversion_factor: float = 1.0
@dataclass
class MatchingResult:
"""Complete matching result."""
total_elements: int
matched: int
unmatched: int
high_confidence: int
needs_review: int
matches: List[QuantityMatch]
unmatched_elements: List[Dict[str, Any]]
# Category to work item mapping rules
CATEGORY_MAPPING = {
# Revit categories to CWICR prefixes
'walls': ['WALL', 'MSNR', 'PART'],
'floors': ['CONC', 'FLOOR', 'SLAB'],
'columns': ['CONC', 'STRL', 'COLM'],
'beams': ['CONC', 'STRL', 'BEAM'],
'foundations': ['CONC', 'FNDN', 'EXCV'],
'roofs': ['ROOF', 'INSUL'],
'doors': ['DOOR', 'CARP'],
'windows': ['WIND', 'GLAZ'],
'stairs': ['STAIR', 'CONC'],
'railings': ['RAIL', 'METL'],
'ceilings': ['CEIL', 'FINI'],
'structural framing': ['STRL', 'STEE'],
'structural columns': ['STRL', 'COLM'],
'pipes': ['PLMB', 'PIPE'],
'ducts': ['HVAC', 'DUCT'],
'conduits': ['ELEC', 'COND'],
'cable trays': ['ELEC', 'CABL'],
'concrete': ['CONC'],
'rebar': ['REBAR', 'RENF'],
'formwork': ['FORM', 'CONC'],
}
# Unit conversion mapping
UNIT_CONVERSIONS = {
('sf', 'm2'): 0.092903,
('m2', 'sf'): 10.7639,
('cy', 'm3'): 0.764555,
('m3', 'cy'): 1.30795,
('lf', 'm'): 0.3048,
('m', 'lf'): 3.28084,
('lb', 'kg'): 0.453592,
('kg', 'lb'): 2.20462,
}
class CWICRQuantityMatcher:
"""Match BIM quantities to CWICR work items."""
def __init__(self, cwicr_data: pd.DataFrame):
self.work_items = cwicr_data
self._index_data()
self._build_search_index()
def _index_data(self):
"""Index work items."""
if 'work_item_code' in self.work_items.columns:
self._code_index = self.work_items.set_index('work_item_code')
else:
self._code_index = None
def _build_search_index(self):
"""Build search index for semantic matching."""
self._search_index = {}
if 'description' in self.work_items.columns:
for _, row in self.work_items.iterrows():
code = row.get('work_item_code', '')
desc = str(row.get('description', '')).lower()
# Index by keywords
words = re.findall(r'\w+', desc)
for word in words:
if len(word) > 3:
if word not in self._search_index:
self._search_index[word] = []
self._search_index[word].append(code)
def _get_category_codes(self, category: str) -> List[str]:
"""Get potential work item prefixes for BIM category."""
cat_lower = category.lower().strip()
for key, prefixes in CATEGORY_MAPPING.items():
if key in cat_lower:
return prefixes
return []
def _semantic_match(self, description: str, category: str) -> List[Tuple[str, float]]:
"""Find work items using semantic matching."""
desc_lower = description.lower()
words = re.findall(r'\w+', desc_lower)
# Find candidate codes
candidates = {}
for word in words:
if word in self._search_index:
for code in self._search_index[word]:
if code not in candidates:
candidates[code] = 0
candidates[code] += 1
# Score candidates
scored = []
for code, count in candidates.items():
if self._code_index is not None and code in self._code_index.index:
item_desc = str(self._code_index.loc[code].get('description', ''))
similarity = SequenceMatcher(None, desc_lower, item_desc.lower()).ratio()
score = (count * 0.4) + (similarity * 0.6)
scored.append((code, score))
return sorted(scored, key=lambda x: x[1], reverse=True)[:5]
def _get_confidence(self, score: float) -> MatchConfidence:
"""Determine confidence level from score."""
if score >= 0.9:
return MatchConfidence.HIGH
elif score >= 0.7:
return MatchConfidence.MEDIUM
elif score >= 0.5:
return MatchConfidence.LOW
else:
return MatchConfidence.MANUAL
def _get_unit_conversion(self, from_unit: str, to_unit: str) -> float:
"""Get unit conversion factor."""
from_norm = from_unit.lower().strip()
to_norm = to_unit.lower().strip()
if from_norm == to_norm:
return 1.0
return UNIT_CONVERSIONS.get((from_norm, to_norm), 1.0)
def match_element(self,
element: Dict[str, Any],
element_id_col: str = 'ElementId',
category_col: str = 'Category',
description_col: str = 'Description',
quantity_col: str = 'Quantity',
unit_col: str = 'Unit') -> Optional[QuantityMatch]:
"""Match single BIM element to work item."""
element_id = str(element.get(element_id_col, ''))
category = str(element.get(category_col, ''))
description = str(element.get(description_col, ''))
quantity = float(element.get(quantity_col, 0) or 0)
unit = str(element.get(unit_col, ''))
# Try category-based matching first
category_prefixes = self._get_category_codes(category)
best_match = None
best_score = 0
match_method = MatchMethod.CATEGORY
if category_prefixes:
# Filter work items by prefix
for prefix in category_prefixes:
matches = self.work_items[
self.work_items['work_item_code'].str.startswith(prefix)
]
for _, item in matches.iterrows():
item_desc = str(item.get('description', ''))
similarity = SequenceMatcher(None, description.lower(), item_desc.lower()).ratio()
if similarity > best_score:
best_score = similarity
best_match = item
# If no good match, try semantic matching
if best_score < 0.5:
semantic_matches = self._semantic_match(description, category)
if semantic_matches:
top_code, top_score = semantic_matches[0]
if top_score > best_score:
best_match = self._code_index.loc[top_code]
best_score = top_score
match_method = MatchMethod.SEMANTIC
if best_match is None or best_score < 0.3:
return None
# Get unit conversion
work_item_unit = str(best_match.get('unit', ''))
conversion = self._get_unit_conversion(unit, work_item_unit)
return QuantityMatch(
bim_element_id=element_id,
bim_category=category,
bim_description=description,
bim_quantity=quantity,
bim_unit=unit,
matched_work_item=str(best_match.get('work_item_code', best_match.name)),
work_item_description=str(best_match.get('description', '')),
work_item_unit=work_item_unit,
match_method=match_method,
confidence=self._get_confidence(best_score),
confidence_score=round(best_score, 2),
unit_conversion_factor=conversion
)
def match_quantities(self,
bim_data: pd.DataFrame,
element_id_col: str = 'ElementId',
category_col: str = 'Category',
description_col: str = 'Description',
quantity_col: str = 'Quantity',
unit_col: str = 'Unit') -> MatchingResult:
"""Match all BIM quantities to work items."""
matches = []
unmatched = []
for _, row in bim_data.iterrows():
element = row.to_dict()
match = self.match_element(
element,
element_id_col,
category_col,
description_col,
quantity_col,
unit_col
)
if match:
matches.append(match)
else:
unmatched.append(element)
return MatchingResult(
total_elements=len(bim_data),
matched=len(matches),
unmatched=len(unmatched),
high_confidence=len([m for m in matches if m.confidence == MatchConfidence.HIGH]),
needs_review=len([m for m in matches if m.confidence == MatchConfidence.MANUAL]),
matches=matches,
unmatched_elements=unmatched
)
def apply_custom_mapping(self,
result: MatchingResult,
mapping: Dict[str, str]) -> MatchingResult:
"""Apply custom category to work item mapping."""
updated_matches = []
for match in result.matches:
if match.bim_category in mapping:
# Override with custom mapping
code = mapping[match.bim_category]
if self._code_index is not None and code in self._code_index.index:
item = self._code_index.loc[code]
match.matched_work_item = code
match.work_item_description = str(item.get('description', ''))
match.work_item_unit = str(item.get('unit', ''))
match.match_method = MatchMethod.RULE_BASED
match.confidence = MatchConfidence.HIGH
match.confidence_score = 1.0
updated_matches.append(match)
result.matches = updated_matches
return result
def export_matches(self,
result: MatchingResult,
output_path: str) -> str:
"""Export matching results to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Total Elements': result.total_elements,
'Matched': result.matched,
'Unmatched': result.unmatched,
'High Confidence': result.high_confidence,
'Needs Review': result.needs_review,
'Match Rate %': round(result.matched / result.total_elements * 100, 1) if result.total_elements > 0 else 0
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Matches
matches_df = pd.DataFrame([
{
'BIM Element ID': m.bim_element_id,
'BIM Category': m.bim_category,
'BIM Description': m.bim_description,
'BIM Quantity': m.bim_quantity,
'BIM Unit': m.bim_unit,
'Work Item Code': m.matched_work_item,
'Work Item Description': m.work_item_description,
'Work Item Unit': m.work_item_unit,
'Converted Quantity': m.bim_quantity * m.unit_conversion_factor,
'Match Method': m.match_method.value,
'Confidence': m.confidence.value,
'Score': m.confidence_score
}
for m in result.matches
])
matches_df.to_excel(writer, sheet_name='Matches', index=False)
# Needs Review
review_df = matches_df[matches_df['Confidence'].isin(['low', 'manual'])]
review_df.to_excel(writer, sheet_name='Needs Review', index=False)
# Unmatched
unmatched_df = pd.DataFrame(result.unmatched_elements)
unmatched_df.to_excel(writer, sheet_name='Unmatched', index=False)
return output_path
def generate_cost_linked_qto(self,
result: MatchingResult) -> pd.DataFrame:
"""Generate cost-linked QTO from matches."""
data = []
for match in result.matches:
if self._code_index is not None and match.matched_work_item in self._code_index.index:
item = self._code_index.loc[match.matched_work_item]
converted_qty = match.bim_quantity * match.unit_conversion_factor
labor = float(item.get('labor_cost', 0) or 0)
material = float(item.get('material_cost', 0) or 0)
equipment = float(item.get('equipment_cost', 0) or 0)
unit_cost = labor + material + equipment
data.append({
'Work Item Code': match.matched_work_item,
'Description': match.work_item_description,
'Unit': match.work_item_unit,
'Quantity': round(converted_qty, 2),
'Unit Cost': round(unit_cost, 2),
'Total Cost': round(converted_qty * unit_cost, 2),
'BIM Elements': 1,
'Confidence': match.confidence.value
})
df = pd.DataFrame(data)
# Aggregate by work item
if not df.empty:
aggregated = df.groupby(['Work Item Code', 'Description', 'Unit']).agg({
'Quantity': 'sum',
'Unit Cost': 'first',
'BIM Elements': 'sum'
}).reset_index()
aggregated['Total Cost'] = aggregated['Quantity'] * aggregated['Unit Cost']
return aggregated
return dfQuick Start
# Load CWICR data
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
# Initialize matcher
matcher = CWICRQuantityMatcher(cwicr)
# Load BIM quantities
bim_qto = pd.read_excel("revit_quantities.xlsx")
# Match quantities
result = matcher.match_quantities(bim_qto)
print(f"Matched: {result.matched}/{result.total_elements}")
print(f"High Confidence: {result.high_confidence}")
print(f"Needs Review: {result.needs_review}")Common Use Cases
1. Generate Cost-Linked QTO
qto_with_costs = matcher.generate_cost_linked_qto(result)
print(f"Total Cost: ${qto_with_costs['Total Cost'].sum():,.2f}")2. Custom Mapping Rules
custom_mapping = {
'Walls': 'WALL-001',
'Floors': 'CONC-002',
'Structural Columns': 'STRL-003'
}
result = matcher.apply_custom_mapping(result, custom_mapping)3. Export Results
matcher.export_matches(result, "quantity_matching.xlsx")Resources
- GitHub: OpenConstructionEstimate-DDC-CWICR
- DDC Book: Chapter 2.3 - BIM-to-Cost Integration