Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
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Build no-code/low-code automation workflows for construction using n8n. Automate data extraction, cost estimation, report generation, and system integrations without writing code.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill implements visual workflow automation for construction processes using n8n. Automate repetitive tasks, integrate systems, and build PROJECT TO BUDGET pipelines without extensive programming.
Inspired by DDC Methodology - Automating the bridge between BIM models and cost estimation.
"Автоматизация процесса 'от проекта к смете' позволяет сократить время на подготовку бюджета с недель до часов." — DDC LinkedIn Post
# Using Docker (recommended)
docker run -it --rm \
--name n8n \
-p 5678:5678 \
-v ~/.n8n:/home/node/.n8n \
n8nio/n8n
# Using npm
npm install n8n -g
n8n start
# Access at: http://localhost:5678
{
"name": "Revit to Budget Automation",
"nodes": [
{
"name": "Watch Revit Export Folder",
"type": "n8n-nodes-base.localFileTrigger",
"parameters": {
"path": "/data/revit_exports",
"events": ["add"],
"fileExtension": ".xlsx"
}
},
{
"name": "Read Excel Data",
"type": "n8n-nodes-base.readWriteFile",
"parameters": {
"operation": "read",
"filePath": "={{ $json.fileName }}"
}
},
{
"name": "Parse BIM Elements",
"type": "n8n-nodes-base.code",
"parameters": {
"language": "python",
"code": "import pandas as pd\nimport json\n\ndf = pd.read_excel(items[0].binary.data)\n\nelements = df.to_dict('records')\n\nreturn [{'json': {'elements': elements, 'count': len(elements)}}]"
}
},
{
"name": "Match to Unit Prices",
"type": "n8n-nodes-base.httpRequest",
"parameters": {
"url": "http://api.construction-prices.com/match",
"method": "POST",
"body": "={{ JSON.stringify($json.elements) }}"
}
},
{
"name": "Calculate Costs",
"type": "n8n-nodes-base.code",
"parameters": {
"language": "javascript",
"code": "const elements = items[0].json.elements;\n\nlet totalCost = 0;\nconst costBreakdown = [];\n\nfor (const elem of elements) {\n const cost = elem.quantity * elem.unit_price;\n totalCost += cost;\n costBreakdown.push({\n category: elem.category,\n quantity: elem.quantity,\n unit_price: elem.unit_price,\n total: cost\n });\n}\n\nreturn [{\n json: {\n total_cost: totalCost,\n breakdown: costBreakdown\n }\n}];"
}
},
{
"name": "Generate Report",
"type": "n8n-nodes-base.spreadsheetFile",
"parameters": {
"operation": "create",
"fileName": "cost_estimate_{{ $now.format('yyyy-MM-dd') }}.xlsx"
}
},
{
"name": "Send Email Notification",
"type": "n8n-nodes-base.emailSend",
"parameters": {
"to": "project-team@company.com",
"subject": "New Cost Estimate Generated",
"text": "Total estimate: ${{ $json.total_cost }}"
}
}
]
}
{
"name": "Daily Project Report",
"nodes": [
{
"name": "Schedule Trigger",
"type": "n8n-nodes-base.cron",
"parameters": {
"cronExpression": "0 6 * * 1-5"
}
},
{
"name": "Fetch Project Data",
"type": "n8n-nodes-base.httpRequest",
"parameters": {
"url": "{{ $env.PROJECT_API }}/status",
"method": "GET"
}
},
{
"name": "Fetch Weather Data",
"type": "n8n-nodes-base.httpRequest",
"parameters": {
"url": "https://api.openweathermap.org/data/2.5/weather",
"qs": {
"q": "{{ $json.project_location }}",
"appid": "{{ $env.WEATHER_API_KEY }}"
}
}
},
{
"name": "Generate Report",
"type": "n8n-nodes-base.code",
"parameters": {
"language": "javascript",
"code": "const project = items[0].json;\nconst weather = items[1].json;\n\nconst report = {\n date: new Date().toISOString().split('T')[0],\n project_name: project.name,\n progress: project.progress_pct,\n weather: {\n condition: weather.weather[0].main,\n temp: Math.round(weather.main.temp - 273.15)\n },\n tasks_today: project.scheduled_tasks,\n blockers: project.blockers || []\n};\n\nreturn [{ json: report }];"
}
},
{
"name": "Post to Slack",
"type": "n8n-nodes-base.slack",
"parameters": {
"channel": "#project-updates",
"text": "📊 Daily Report - {{ $json.project_name }}\n\nProgress: {{ $json.progress }}%\n🌡️ Weather: {{ $json.weather.condition }} ({{ $json.weather.temp }}°C)\n\nToday's Tasks:\n{{ $json.tasks_today.join('\\n') }}"
}
}
]
}
{
"name": "BIM Change Detection",
"nodes": [
{
"name": "Watch IFC Folder",
"type": "n8n-nodes-base.localFileTrigger",
"parameters": {
"path": "/models",
"events": ["change"],
"fileExtension": ".ifc"
}
},
{
"name": "Extract Model Data",
"type": "n8n-nodes-base.executeCommand",
"parameters": {
"command": "python /scripts/extract_ifc.py {{ $json.fileName }}"
}
},
{
"name": "Compare with Previous",
"type": "n8n-nodes-base.code",
"parameters": {
"language": "python",
"code": "import json\n\ncurrent = json.loads(items[0].json.output)\nprevious = load_previous_version()\n\nchanges = {\n 'added': [],\n 'modified': [],\n 'deleted': []\n}\n\n# Compare logic\nfor elem in current:\n if elem['id'] not in previous:\n changes['added'].append(elem)\n elif elem != previous[elem['id']]:\n changes['modified'].append(elem)\n\nfor elem_id in previous:\n if elem_id not in [e['id'] for e in current]:\n changes['deleted'].append(previous[elem_id])\n\nreturn [{'json': changes}]"
}
},
{
"name": "Update Database",
"type": "n8n-nodes-base.postgres",
"parameters": {
"operation": "executeQuery",
"query": "INSERT INTO model_changes (timestamp, changes) VALUES (NOW(), '{{ JSON.stringify($json) }}')"
}
},
{
"name": "Notify Team",
"type": "n8n-nodes-base.microsoftTeams",
"parameters": {
"message": "🔔 Model Updated\n\n+{{ $json.added.length }} elements added\n📝 {{ $json.modified.length }} elements modified\n-{{ $json.deleted.length }} elements deleted"
}
}
]
}
// n8n Code Node - Extract BIM Quantities
const xlsx = require('xlsx');
// Read uploaded file
const workbook = xlsx.read(items[0].binary.data, { type: 'buffer' });
const sheetName = workbook.SheetNames[0];
const data = xlsx.utils.sheet_to_json(workbook.Sheets[sheetName]);
// Process BIM elements
const quantities = {};
for (const row of data) {
const category = row['Category'] || 'Unknown';
const volume = parseFloat(row['Volume']) || 0;
if (!quantities[category]) {
quantities[category] = { count: 0, volume: 0 };
}
quantities[category].count++;
quantities[category].volume += volume;
}
return [{ json: { quantities, total_elements: data.length } }];
// n8n Code Node - Match elements to unit prices
const elements = items[0].json.elements;
const priceDatabase = $env.PRICE_DATABASE;
const matched = [];
for (const elem of elements) {
// Fuzzy match description to price items
const match = await $http.post(`${priceDatabase}/search`, {
query: elem.description,
category: elem.category
});
matched.push({
...elem,
matched_item: match.data.best_match,
unit_price: match.data.unit_price,
confidence: match.data.confidence
});
}
return [{ json: { matched_elements: matched } }];
// n8n Code Node - Generate PDF Report
const PDFDocument = require('pdfkit');
const doc = new PDFDocument();
const buffers = [];
doc.on('data', buffers.push.bind(buffers));
// Header
doc.fontSize(20).text('Cost Estimate Report', { align: 'center' });
doc.moveDown();
// Project Info
doc.fontSize(12).text(`Project: ${items[0].json.project_name}`);
doc.text(`Date: ${new Date().toLocaleDateString()}`);
doc.moveDown();
// Cost Summary
doc.fontSize(14).text('Cost Summary', { underline: true });
for (const [category, cost] of Object.entries(items[0].json.costs)) {
doc.fontSize(10).text(`${category}: $${cost.toLocaleString()}`);
}
doc.end();
return new Promise(resolve => {
doc.on('end', () => {
resolve([{
json: { success: true },
binary: {
data: Buffer.concat(buffers).toString('base64'),
fileName: 'cost_report.pdf',
mimeType: 'application/pdf'
}
}]);
});
});
Data Sources:
- Google Sheets: Project tracking, cost databases
- Airtable: Element databases, issue tracking
- PostgreSQL: BIM databases, project data
- HTTP Request: API integrations
File Processing:
- Read/Write File: Excel, CSV, JSON
- Execute Command: Python scripts, CLI tools
- Code: Custom processing logic
Communication:
- Slack: Team notifications
- Microsoft Teams: Project updates
- Email: Reports, alerts
- Telegram: Mobile notifications
Cloud Storage:
- AWS S3: Model storage
- Google Drive: Document sharing
- Dropbox: File sync
{
"workflow": "QTO Extraction",
"trigger": "Manual/Webhook",
"steps": [
"Receive IFC file",
"Extract quantities (Python/IfcOpenShell)",
"Group by category",
"Add unit prices",
"Calculate totals",
"Generate Excel report",
"Upload to cloud storage",
"Send notification"
]
}
{
"workflow": "Daily Status",
"trigger": "Cron (6:00 AM)",
"steps": [
"Fetch project status from API",
"Get weather forecast",
"Check scheduled tasks",
"Compile daily report",
"Post to Slack/Teams",
"Email to stakeholders"
]
}
1. **Error Handling**
- Always add error branches
- Log failures to database
- Send alerts on critical failures
2. **Data Validation**
- Validate input data format
- Check for required fields
- Handle missing values gracefully
3. **Performance**
- Use batch processing for large datasets
- Implement pagination for API calls
- Cache frequently used data
4. **Security**
- Store credentials in environment variables
- Use encryption for sensitive data
- Implement access controls
| Workflow Type | Trigger | Common Nodes |
|---|---|---|
| File Processing | File Trigger | Code, HTTP, Spreadsheet |
| Scheduled Reports | Cron | HTTP, Code, Email |
| Data Sync | Webhook | Database, API, Code |
| Notifications | Various | Slack, Teams, Email |
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Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
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Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.