--- slug: "n8n-cost-estimation" display_name: "N8N Cost Estimation" description: "Build n8n pipeline for automated cost estimation from Revit/IFC using DDC CWICR database and LLM classification." --- # Automated Cost Estimation Pipeline ## Business Case ### Problem Statement Traditional cost estimation requires: - Manual work item lookup in price databases - Time-consuming element classification - Expert knowledge of pricing standards - Repetitive data entry ### Solution Free open-source n8n pipeline that converts CAD (Revit 2015-2026) files into full cost and time estimates using AI (LLM) and vector database with 55,000+ work items. ### Business Value | Traditional Role | Automated Alternative | |-----------------|----------------------| | BIM Manager manually exports data | Pipeline auto-classifies elements | | Junior Estimator searches databases | Vector search finds matches in ms | | Senior Estimator maps assemblies | LLM identifies quantity parameters | | Foreman calculates labor hours | DDC CWICR contains documented norms | | Project Manager aggregates costs | Pipeline outputs phased breakdown | **Processing speed**: 3-10 seconds per element group ## Technical Implementation ### Pipeline Architecture ``` ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Revit/IFC │───>│ CAD2DATA │───>│ Structured │ │ File │ │ Converter │ │ Excel/CSV │ └─────────────┘ └─────────────┘ └─────────────┘ │ ▼ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Cost Report │<───│ Price Match │<───│ LLM Class. │ │ HTML/Excel │ │ DDC CWICR │ │ + QTO │ └─────────────┘ └─────────────┘ └─────────────┘ ``` ### n8n Pipeline Steps #### 1. File Conversion Node ```javascript // Execute CAD converter const filePath = $input.first().json.file_path; const outputDir = filePath.replace(/\.[^.]+$/, ''); const command = `RvtExporter.exe "${filePath}" complete bbox`; // Returns: { xlsx_path, dae_path } ``` #### 2. Load Elements ```javascript // Read converted Excel into n8n const xlsx = $node["Read Binary Files"].json; const elements = xlsx.sheets["Elements"]; // Group by category for processing const grouped = elements.reduce((acc, el) => { const cat = el.Category; if (!acc[cat]) acc[cat] = []; acc[cat].push(el); return acc; }, {}); return Object.entries(grouped).map(([category, items]) => ({ json: {category, items, count: items.length} })); ``` #### 3. LLM Classification ```javascript // Prompt for Claude/GPT classification const prompt = ` You are a construction estimator. Given these BIM elements: Category: ${$input.first().json.category} Sample elements: ${JSON.stringify($input.first().json.items.slice(0,5))} 1. Identify the construction work type 2. List relevant quantity parameters (Volume, Area, Length, Count) 3. Suggest standard work items from construction norms Return as JSON: { "work_type": "...", "quantity_params": ["Volume", "Area"], "suggested_items": ["Concrete foundation", "Formwork"] } `; ``` #### 4. Vector Search in CWICR ```javascript // Search DDC CWICR database for matching work items const qdrantClient = require('@qdrant/js-client-rest'); const searchResults = await qdrantClient.search('ddc_cwicr_en', { vector: await getEmbedding($input.first().json.work_description), limit: 10, score_threshold: 0.7 }); return searchResults.map(r => ({ json: { work_code: r.payload.work_item_code, description: r.payload.description, unit: r.payload.unit, unit_price: r.payload.unit_price, similarity: r.score } })); ``` #### 5. Calculate Costs ```javascript // Match quantities to prices const elements = $node["Load Elements"].json; const prices = $node["Vector Search"].json; let totalCost = 0; const breakdown = []; for (const el of elements.items) { const matchedPrice = prices.find(p => p.similarity > 0.8); if (matchedPrice) { const quantity = el.Volume || el.Area || 1; const cost = quantity * matchedPrice.unit_price; totalCost += cost; breakdown.push({ element: el.Name, quantity: quantity, unit: matchedPrice.unit, unit_price: matchedPrice.unit_price, total: cost }); } } return [{json: {totalCost, breakdown}}]; ``` #### 6. Generate Report ```javascript // Create HTML report const data = $input.first().json; const html = ` Cost Estimate Report

Cost Estimate Report

Total: $${data.totalCost.toLocaleString()}

${data.breakdown.map(row => ` `).join('')}

Element	Quantity	Unit	Price	Total
${row.element}	${row.quantity.toFixed(2)}	${row.unit}	$${row.unit_price.toFixed(2)}	$${row.total.toFixed(2)}

`; return [{json: {html, filename: 'estimate_report.html'}}]; ``` ## Real-World Results Example project (rac_basic_sample.rvt): - Processing time: ~30 minutes with ChatGPT - Elements analyzed: 500+ - Automatic classification: 95% accuracy - Manual review needed: 5% edge cases ## Key Insight from Community > "My subjective take: professionals who ignore workflow automation and AI-agents today have roughly 5 years before the construction industry moves past them. The tools are free and open. The data is open. The only question is who learns to use them first." ## Prerequisites - n8n (local or hosted) - DDC CAD converters - DDC CWICR database - OpenAI/Anthropic API key - Qdrant vector database ## Resources - **GitHub**: cad2data Pipeline repository - **Database**: OpenConstructionEstimate-DDC-CWICR - **Community**: n8n Workflows for Construction (Telegram)

Automated Cost Estimation Pipeline

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