johnalbertini14-glitch BIM Cost Estimation with DDC CWICR
Generate accurate cost estimates from BIM models using AI classification and the DDC CWICR construction cost database.
Business Case
Problem: Traditional cost estimation:
- Manual and time-consuming (weeks for detailed estimate)
- Subjective and inconsistent between estimators
- Requires specialized knowledge
- Difficult to update with design changes
Solution: Automated BIM-to-cost pipeline:
- Extract quantities directly from model
- AI classifies elements to work items
- Vector search finds matching prices in CWICR
- Complete estimate in hours, not weeks
ROI: 80% reduction in estimation time, consistent methodology
System Architecture
┌──────────────────────────────────────────────────────────────────────────┐
│ BIM TO COST ESTIMATION PIPELINE │
├──────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────────────┐ │
│ │ BIM │ │ DDC │ │ AI │ │ DDC CWICR │ │
│ │ Model │────►│Converter│────►│ LLM │────►│ Vector Search │ │
│ │.rvt/.ifc│ │ │ │ │ │ (Qdrant) │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ ┌──────────┐ │
│ │ .xlsx │ │ Work │ │ Matched │ │
│ │ QTO │ │ Items │ │ Rates │ │
│ └─────────┘ └─────────┘ └──────────┘ │
│ │ │ │ │
│ └──────────────┼────────────────────┘ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ COST ESTIMATE │ │
│ │ │ │
│ │ • By element │ │
│ │ • By trade │ │
│ │ • By phase │ │
│ │ • Resources │ │
│ └─────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────────┘
DDC CWICR Database
Database Overview:
work_items: 55,719
resources: 27,672
languages: 9 (AR, DE, EN, ES, FR, HI, PT, RU, ZH)
fields_per_item: 85
embedding_model: text-embedding-3-large (3072d)
vector_db: Qdrant
Collections:
- ddc_cwicr_ar # Arabic (Dubai prices)
- ddc_cwicr_de # German (Berlin prices)
- ddc_cwicr_en # English (Toronto prices)
- ddc_cwicr_es # Spanish (Barcelona prices)
- ddc_cwicr_fr # French (Paris prices)
- ddc_cwicr_hi # Hindi (Mumbai prices)
- ddc_cwicr_pt # Portuguese (São Paulo prices)
- ddc_cwicr_ru # Russian (St. Petersburg prices)
- ddc_cwicr_zh # Chinese (Shanghai prices)
Pipeline Stages
| Stage | Name | Description |
|---|---|---|
| 0 | Collect BIM Data | Extract elements from Revit/IFC |
| 1 | Project Detection | AI identifies project type |
| 2 | Phase Generation | AI creates construction phases |
| 3 | Element Assignment | AI maps types to phases |
| 4 | Work Decomposition | AI breaks types into work items |
| 5 | Vector Search | Find matching rates in CWICR |
| 6 | Unit Mapping | Convert BIM units to rate units |
| 7 | Cost Calculation | Qty × Unit Price |
| 7.5 | Validation | CTO review for completeness |
| 8 | Aggregation | Sum by phases and categories |
| 9 | Report Generation | HTML and Excel outputs |
Python Implementation
import pandas as pd
import numpy as np
from qdrant_client import QdrantClient
from qdrant_client.models import Filter, FieldCondition, MatchValue
from openai import OpenAI
from typing import List, Dict, Optional
from dataclasses import dataclass
import json
@dataclass
class WorkItem:
"""Matched work item from CWICR"""
cwicr_code: str
description: str
unit: str
unit_price: float
labor_cost: float
material_cost: float
equipment_cost: float
productivity: float # units per hour
currency: str
confidence: float
@dataclass
class CostLineItem:
"""Single line item in estimate"""
bim_type: str
work_item: WorkItem
quantity: float
quantity_unit: str
total_cost: float
labor_cost: float
material_cost: float
equipment_cost: float
phase: str
trade: str
class BIMCostEstimator:
"""BIM to cost estimation using DDC CWICR"""
def __init__(
self,
qdrant_url: str,
qdrant_api_key: str = None,
openai_api_key: str = None,
language: str = "EN"
):
self.qdrant = QdrantClient(url=qdrant_url, api_key=qdrant_api_key)
self.openai = OpenAI(api_key=openai_api_key)
self.language = language
self.collection = f"ddc_cwicr_{language.lower()}"
def get_embedding(self, text: str) -> List[float]:
"""Generate embedding for text"""
response = self.openai.embeddings.create(
model="text-embedding-3-large",
input=text,
dimensions=3072
)
return response.data[0].embedding
def search_cwicr(
self,
query: str,
limit: int = 5,
category_filter: str = None
) -> List[WorkItem]:
"""Search CWICR database for matching work items"""
# Get embedding
query_vector = self.get_embedding(query)
# Build filter if category specified
query_filter = None
if category_filter:
query_filter = Filter(
must=[
FieldCondition(
key="category",
match=MatchValue(value=category_filter)
)
]
)
# Search
results = self.qdrant.search(
collection_name=self.collection,
query_vector=query_vector,
query_filter=query_filter,
limit=limit
)
# Parse results
work_items = []
for r in results:
payload = r.payload
work_items.append(WorkItem(
cwicr_code=payload.get('code', ''),
description=payload.get('description', ''),
unit=payload.get('unit', ''),
unit_price=float(payload.get('unit_price', 0)),
labor_cost=float(payload.get('labor_cost', 0)),
material_cost=float(payload.get('material_cost', 0)),
equipment_cost=float(payload.get('equipment_cost', 0)),
productivity=float(payload.get('productivity', 1)),
currency=payload.get('currency', 'USD'),
confidence=r.score
))
return work_items
def decompose_bim_type(
self,
bim_type: str,
category: str
) -> List[str]:
"""Use LLM to decompose BIM type into work items"""
prompt = f"""
Decompose this BIM element type into construction work items:
BIM Type: {bim_type}
Category: {category}
List the individual work activities needed to construct this element.
For example, "Brick Wall 240mm" decomposes into:
- Masonry: Brick laying
- Mortar: Cement mortar for joints
- Plaster: Internal plaster finish
- Paint: Wall painting
Return a JSON array of work item descriptions.
Example: ["Brick masonry laying", "Cement mortar for brick joints", "Internal cement plaster 15mm"]
"""
response = self.openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}],
response_format={"type": "json_object"}
)
try:
result = json.loads(response.choices[0].message.content)
return result.get('work_items', [bim_type])
except:
return [bim_type]
def estimate_element(
self,
bim_type: str,
category: str,
quantity: float,
quantity_unit: str,
phase: str = "Construction"
) -> List[CostLineItem]:
"""Estimate cost for single BIM element type"""
# Decompose into work items
work_descriptions = self.decompose_bim_type(bim_type, category)
line_items = []
for work_desc in work_descriptions:
# Search CWICR for matching rate
matches = self.search_cwicr(work_desc, limit=1)
if not matches:
continue
best_match = matches[0]
# Convert quantity if units don't match
adjusted_qty = self._convert_units(
quantity, quantity_unit, best_match.unit
)
# Calculate costs
total = adjusted_qty * best_match.unit_price
labor = adjusted_qty * best_match.labor_cost
material = adjusted_qty * best_match.material_cost
equipment = adjusted_qty * best_match.equipment_cost
line_items.append(CostLineItem(
bim_type=bim_type,
work_item=best_match,
quantity=adjusted_qty,
quantity_unit=best_match.unit,
total_cost=total,
labor_cost=labor,
material_cost=material,
equipment_cost=equipment,
phase=phase,
trade=self._get_trade(category)
))
return line_items
def estimate_from_qto(
self,
qto_data: pd.DataFrame,
type_column: str = "Type Name",
category_column: str = "Category",
quantity_column: str = "Volume"
) -> List[CostLineItem]:
"""Generate estimate from QTO DataFrame"""
all_line_items = []
# Group by type
grouped = qto_data.groupby([category_column, type_column]).agg({
quantity_column: 'sum'
}).reset_index()
for _, row in grouped.iterrows():
items = self.estimate_element(
bim_type=row[type_column],
category=row[category_column],
quantity=row[quantity_column],
quantity_unit="m³" # Assume volume, adjust based on category
)
all_line_items.extend(items)
return all_line_items
def _convert_units(
self,
value: float,
from_unit: str,
to_unit: str
) -> float:
"""Convert between units"""
# Simplified conversion - expand as needed
conversions = {
('m³', 'm³'): 1.0,
('m²', 'm²'): 1.0,
('m', 'm'): 1.0,
('ft³', 'm³'): 0.0283168,
('ft²', 'm²'): 0.092903,
('ft', 'm'): 0.3048,
}
key = (from_unit.lower(), to_unit.lower())
factor = conversions.get(key, 1.0)
return value * factor
def _get_trade(self, category: str) -> str:
"""Map BIM category to trade"""
trade_map = {
'Walls': 'Masonry',
'Floors': 'Concrete',
'Structural Columns': 'Concrete',
'Structural Framing': 'Steel',
'Doors': 'Carpentry',
'Windows': 'Glazing',
'Plumbing Fixtures': 'Plumbing',
'Electrical Equipment': 'Electrical',
'Mechanical Equipment': 'HVAC'
}
return trade_map.get(category, 'General')
def generate_estimate_report(
self,
line_items: List[CostLineItem],
project_name: str,
output_path: str
) -> dict:
"""Generate comprehensive estimate report"""
# Convert to DataFrame
records = []
for item in line_items:
records.append({
'BIM Type': item.bim_type,
'Work Item': item.work_item.description,
'CWICR Code': item.work_item.cwicr_code,
'Quantity': round(item.quantity, 2),
'Unit': item.quantity_unit,
'Unit Price': round(item.work_item.unit_price, 2),
'Labor': round(item.labor_cost, 2),
'Material': round(item.material_cost, 2),
'Equipment': round(item.equipment_cost, 2),
'Total': round(item.total_cost, 2),
'Phase': item.phase,
'Trade': item.trade,
'Currency': item.work_item.currency,
'Confidence': round(item.work_item.confidence, 2)
})
df = pd.DataFrame(records)
# Calculate totals
total_cost = df['Total'].sum()
total_labor = df['Labor'].sum()
total_material = df['Material'].sum()
total_equipment = df['Equipment'].sum()
# Summary by trade
by_trade = df.groupby('Trade')['Total'].sum().sort_values(ascending=False)
# Write Excel
excel_path = f"{output_path}/{project_name}_Estimate.xlsx"
with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
# Summary sheet
summary_data = {
'Metric': ['Total Cost', 'Labor Cost', 'Material Cost', 'Equipment Cost'],
'Value': [total_cost, total_labor, total_material, total_equipment]
}
pd.DataFrame(summary_data).to_excel(writer, sheet_name='Summary', index=False)
# By Trade
by_trade.to_frame().to_excel(writer, sheet_name='By Trade')
# Detail
df.to_excel(writer, sheet_name='Detail', index=False)
return {
'excel_path': excel_path,
'total_cost': total_cost,
'total_labor': total_labor,
'total_material': total_material,
'total_equipment': total_equipment,
'by_trade': by_trade.to_dict(),
'line_items': len(df),
'currency': line_items[0].work_item.currency if line_items else 'USD'
}
# Usage Example
def estimate_from_bim_model(
model_path: str,
qdrant_url: str,
language: str = "EN",
output_dir: str = "."
) -> dict:
"""Complete BIM to cost estimation workflow"""
import subprocess
from pathlib import Path
# Step 1: Convert BIM to Excel
print("Converting BIM model...")
subprocess.run([
r"C:\DDC\RvtExporter.exe",
model_path,
"complete", "bbox"
])
xlsx_path = Path(model_path).with_suffix('.xlsx')
# Step 2: Load QTO data
print("Loading quantity data...")
df = pd.read_excel(xlsx_path)
# Step 3: Initialize estimator
estimator = BIMCostEstimator(
qdrant_url=qdrant_url,
language=language
)
# Step 4: Generate estimate
print("Generating cost estimate...")
line_items = estimator.estimate_from_qto(df)
# Step 5: Generate report
project_name = Path(model_path).stem
result = estimator.generate_estimate_report(
line_items=line_items,
project_name=project_name,
output_path=output_dir
)
print(f"\nEstimate Complete!")
print(f"Total Cost: {result['currency']} {result['total_cost']:,.2f}")
print(f"Excel Report: {result['excel_path']}")
return result
if __name__ == "__main__":
result = estimate_from_bim_model(
model_path=r"C:\Projects\Building.rvt",
qdrant_url="https://your-qdrant-instance.io",
language="DE",
output_dir=r"C:\Projects\Estimates"
)
n8n Workflow
See: n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json
stages:
- convert: RvtExporter → XLSX
- detect_project: LLM identifies project type
- generate_phases: LLM creates construction phases
- decompose: LLM breaks types into work items
- vector_search: Qdrant finds CWICR matches
- calculate: Qty × Unit Price
- validate: CTO review
- report: HTML + Excel output
Output Example
╔══════════════════════════════════════════════════════════════╗
║ COST ESTIMATE SUMMARY ║
║ Project: Office Building Berlin ║
║ Date: 2026-01-24 ║
╠══════════════════════════════════════════════════════════════╣
TOTAL PROJECT COST: EUR 4,523,678.00
───────────────────────────────────────────────────────────────
Labor: EUR 1,847,234.00 (41%)
Materials: EUR 2,312,456.00 (51%)
Equipment: EUR 363,988.00 ( 8%)
BY TRADE
───────────────────────────────────────────────────────────────
Concrete: EUR 1,234,567.00 (27%)
Masonry: EUR 876,543.00 (19%)
Steel Structure: EUR 654,321.00 (14%)
MEP: EUR 543,210.00 (12%)
Finishes: EUR 432,109.00 (10%)
Other: EUR 782,928.00 (18%)
CONFIDENCE ANALYSIS
───────────────────────────────────────────────────────────────
High (>0.85): 78%
Medium (0.70-0.85): 18%
Low (<0.70): 4%
╚══════════════════════════════════════════════════════════════╝
Resources
- CWICR Repository: https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR
- Live Demo: https://openconstructionestimate.com
- Qdrant: https://qdrant.tech
"Resource-based costing separates physical quantities from volatile prices, enabling transparent and auditable estimates."