Skill: datadrivenconstruction/bim-cost-estimation-cwicr (version 2.1.0)

--- name: "bim-cost-estimation-cwicr" description: "Automated cost estimation from BIM models using DDC CWICR database with 55,719 work items. AI classification + vector search for accurate pricing." homepage: "https://datadrivenconstruction.io" metadata: {"openclaw":{"emoji":"🏗️","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"],"env":["OPENAI_API_KEY","QDRANT_URL"]},"primaryEnv":"OPENAI_API_KEY"}} --- # 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 ```yaml 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 ```python 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, [REDACTED_SECRET]) self.openai = OpenAI([REDACTED_SECRET]) 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` ```yaml 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."*


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