Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Build no-code/low-code automation workflows for construction using n8n. Automate data extraction, cost estimation, report generation, and system integrations without writing code.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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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概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
日本語の概要は準備中です。原文の説明を表示しています。