GitHub - datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN: Workflow for AI Agents enables automated conversion of CAD files (such as `.rvt`, `.ifc`, `.dwg`) using command-line converters on a local Windows machine

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CAD/BIM (Revit, DWG, IFC, DGN) processing and conversion with batch handling, grouping, checks, cost estimation and QTO reports. Visualization of automation processes in open agents and workflows

Automate your CAD/BIM data extraction and transformation using DDC UI, ComandPromts, Powershell or Workflows with no vendor lock-in, no Autodesk® or CAD licenses, and full control of your project data

DataDrivenConstruction clients and users

Tutorial Videos
Overview
Supported Formats
Key Features
Running the Converters
🖥️ Command Line Interface (CLI)
🚀 AI Integration
- 📂 AI_AGENTS_INSTRUCTIONS Folder
Quick Start
📁 Workflows
Troubleshooting
What is DataFrames?
Excel to Revit. Update Project from Excel
Contributing
🆘 Support
🎓 Consulting and Training

Tutorial Videos

	Universal CAD/BIM Converter Overview Introduction to the DDC Converter for Revit, IFC, DWG, and DGN pipelines – conversion, validation, and automation use cases. Watch Converter Overview on YouTube
	DWG to Excel Converter Pipeline Step-by-step guide on automating DWG to Excel data conversion using `n8n`, making CAD project data easy to use in reporting and analysis. Watch DWG to Excel Pipeline on YouTube
	ETL with Revit and IFC Learn how to build a complete ETL pipeline with Revit and IFC data: extract, transform, validate, and load project information into open formats. Watch ETL with Revit and IFC Tutorial on YouTube
	n8n Quick Start: Easy Installation & Pipeline Creation (Templates and LLM) Step-by-step beginner tutorial on setting up n8n from scratch, building your first automation pipeline, and using LLMs (like ChatGPT/Claude) to generate automations. Watch n8n Quick Start on YouTube
	CAD-BIM Data Pipeline Tutorial Full hands-on walkthrough: automate complex CAD-BIM data processing workflows in `n8n`, including conversion, validation, and actionable analytics. Watch CAD-BIM Pipeline Tutorial on YouTube
	⚡️Automated CAD/BIM Data Validation with n8n \| The End of Manual BIM Checks Discover how to fully automate CAD/BIM data validation workflows using the free, open-source `n8n` platform. Ideal for project teams looking to save hours (or days) every week. Watch Automated Validation Tutorial on YouTube

Overview

This pipeline automates the conversion of CAD/BIM files to Excel for quantity takeoffs, data analysis, and further processing. It supports offline operation and extensibility with Python or AI tools.

Supported Formats

Format	File Extension	Converter	Output
Revit (2015-2026)	`.rvt`	RvtExporter.exe	XLSX database + DAE geometry + Schedules + PDF Drawings
Revit (2015-2026)	`.rvt`	RVT2IFC_converter.exe	IFC2x3, IFC4, IFC4.3, IFCXML, IFCZIP, HDF5
IFC (2x3, 4x1, 4x4, 4x, 4.3)	`.ifc`	IfcExporter.exe	XLSX database + DAE geometry
AutoCAD (1983-2026)	`.dwg`	DwgExporter.exe	XLSX database + PDF Drawings
MicroStation (v7-v8)	`.dgn`	DgnExporter.exe	XLSX database

Key Features

Automated conversion to Excel (elements as rows, properties as columns).
Export of 3D polygonal geometry (DAE) with element IDs matching the XLSX data.
Offline processing without internet, APIs, or licenses.
Extensible for custom post-processing.

⭐ If you find our tools useful and would like to see more similar applications for the construction industry, please give our repositories a star. Star DDC workflow on GitHub and be instantly notified of new releases.

Running the Converters

The DDC converters can be launched in different ways — n8n is just one of the shells for automation.
Depending on your workflow and technical background, you can choose between four methods:

Graphical User Interface (UI)
- Best for non-technical users and quick one-off conversions.
- Intuitive interface, no setup required — just select a folder and start.

Console / Terminal (CMD, PowerShell, Shell)
- Suitable for advanced users, developers, and technical teams.
- Flexible and scriptable, can be integrated into automation scripts or batch processes.

Python or JavaScript Pipelines
- Ideal for enterprises and teams working with large datasets.
- Scalable processing of hundreds of CAD (BIM) files in parallel.
- Ready-to-use examples available in the DDC_Python_pipelines folder.

n8n Workflows
- Best for companies seeking full automation and system integration.
- End-to-end pipelines where CAD (BIM) conversion becomes part of a seamless data flow.
- Examples provided in the DDC_n8n_Workflows&Pipelines folder.

🖥️ Command Line Interface (CLI)

The DDC converters are fully functional command-line tools that can be integrated into any automation workflow. This makes them perfect for scripting, CI/CD pipelines, AI agents, and low-code platforms.

🤖 Why CLI Matters: Let AI Build Your Pipelines

The key advantage of CLI tools is that AI can use them directly.

Modern AI coding assistants (Claude Code, Cursor, GitHub Copilot, Windsurf, Aider, Cline) can execute shell commands, read documentation, and build complete automation pipelines autonomously. This means:

You don't need to write code yourself — just describe what you want, and AI will integrate DDC converters into your workflow.

How it works:

Copy this documentation (or point AI to this README)
Describe your task in natural language: "Convert all Revit files in folder X to Excel, then analyze wall quantities"
AI reads the CLI syntax, writes the script, executes it, and processes the results

What AI can do with DDC converters:

✅ Batch convert hundreds of CAD/BIM files automatically
✅ Build ETL pipelines: Revit → Excel → Database → Dashboard
✅ Create validation scripts that check BIM data quality
✅ Generate reports from extracted data (PDF, HTML, Excel)
✅ Integrate conversions into CI/CD pipelines
✅ Chain multiple tools: convert → validate → classify → estimate costs
✅ Schedule automated processing via cron/Task Scheduler

Example prompt for AI assistant:

I have Revit files in C:\Projects. Using DDC RvtExporter.exe located at C:\DDC\,
convert all .rvt files to Excel with bounding boxes, then create a Python script
that reads the XLSX files and generates a summary report of all wall types and their volumes.

The AI will:

Scan the folder for .rvt files
Execute RvtExporter.exe for each file with correct parameters
Write Python code to parse the resulting .xlsx files
Generate the summary report

This transforms DDC from a tool into an AI-native building block for construction data automation.

RvtExporter.exe — Revit to XLSX/DAE/PDF

===========================================
         DataDrivenConstruction
         DDC Revit Community
         Version: 17.1.1
===========================================

Usage: RvtExporter <input file> [<output file>] [<output file>] [<export mode>] [<category file>] [bbox] [room] [schedule] [sheets2pdf] [-no-xlsx] [-no-collada]

Parameter	Description
`<input file>`	Input `.rvt` / `.rfa` file (required)
`[<output file>]`	Output path for `.dae` file (optional, enabled by default)
`[<output file>]`	Output path for `.xlsx` file (optional, enabled by default)
`[<export mode>]`	`basic` (309 categories), `standard` (724), `complete` (1209), or `custom`
`[<category file>]`	`.txt` file with category names (required only in `custom` mode)
`bbox`	Include element bounding boxes in XLSX output
`room`	Include ToRoom/FromRoom data in XLSX output
`schedule`	Export all Revit schedules
`sheets2pdf`	Export all sheets to PDF
`-no-xlsx`	Disable export to `.xlsx` format
`-no-collada`	Disable export to `.dae` format

Examples:

# Basic conversion (XLSX + DAE)
RvtExporter.exe "C:\Projects\Building.rvt"

# Full export with bounding boxes, schedules, and PDF sheets
RvtExporter.exe "C:\Projects\Building.rvt" complete bbox schedule sheets2pdf

# Export only XLSX (no 3D geometry)
RvtExporter.exe "C:\Projects\Building.rvt" -no-collada

# Custom categories from file
RvtExporter.exe "C:\Projects\Building.rvt" custom "C:\Config\my_categories.txt"

RVT2IFCconverter.exe — Revit to IFC

===========================================
         DataDrivenConstruction
         DDC RVT2IFC Community
         Version: 17.1.2
===========================================

Usage: Rvt2IfcConverter <input.rvt> [<output.ifc>] [preset|mode=<name>] [config="..."] [key=value ...]

Parameter	Description
`<input.rvt>`	Revit file `.rvt` / `.rfa` (required)
`[<output.ifc>]`	Output IFC path (optional)
`preset=<name>` or `mode=<name>`	`standard`, `extended`, `custom`
`config="K=V; K=V; ..."`	Custom configuration (semicolon-separated)
`key=value`	Individual configuration parameters

Examples:

# Standard IFC export
RVT2IFCconverter.exe "C:\Projects\Building.rvt"

# Extended export with more detail
RVT2IFCconverter.exe "C:\Projects\Building.rvt" preset=extended

# Custom output path
RVT2IFCconverter.exe "C:\Projects\Building.rvt" "D:\Output\model.ifc"

# Custom configuration
RVT2IFCconverter.exe "C:\Projects\Building.rvt" config="ExportBaseQuantities=true; SitePlacement=Shared"

Integration Examples

The CLI tools can be called from virtually any environment:

🔹 PowerShell / Batch Scripts

# PowerShell: Process all .rvt files in a folder
Get-ChildItem "C:\Projects\*.rvt" | ForEach-Object {
    & "C:\DDC\RvtExporter.exe" $_.FullName
}

:: Batch: Simple conversion
@echo off
"C:\DDC\RvtExporter.exe" "%1" complete bbox schedule

🔹 VS Code Tasks

Add to .vscode/tasks.json:

{
  "version": "2.0.0",
  "tasks": [
    {
      "label": "Convert Revit to Excel",
      "type": "shell",
      "command": "C:\\DDC\\RvtExporter.exe",
      "args": ["${file}", "complete", "bbox"],
      "problemMatcher": []
    }
  ]
}

🔹 AI Coding Assistants (Claude Code, Cursor, Copilot, Windsurf, Aider, Cline)

AI assistants with terminal access can directly execute DDC converters and build complete workflows:

# Example: AI executes this command when you ask "convert my Revit file to Excel"
RvtExporter.exe "C:\Projects\Model.rvt" complete bbox schedule

Real-world AI workflow scenarios:

You say to AI	AI does
"Convert Building.rvt to Excel with all data"	Runs `RvtExporter.exe Building.rvt complete bbox room`
"Process all Revit files in this folder"	Writes PowerShell loop, executes converter for each file
"Export to IFC 4.3 format"	Runs `RVT2IFCconverter.exe` with correct preset
"Create a cost estimate from this model"	Converts to Excel → parses data → calculates costs
"Validate BIM data quality"	Converts → analyzes XLSX → generates validation report
"Build a dashboard from project data"	Converts → processes with pandas → creates visualization

Supported AI tools:

Claude Code — full terminal access, can run converters and analyze results
Cursor — IDE with AI that can execute shell commands
GitHub Copilot CLI — command-line AI assistant
Windsurf — AI-powered IDE with terminal integration
Aider — AI pair programming in terminal
Cline — VS Code extension with shell access
Open Interpreter — AI that runs code locally
AutoGPT / AgentGPT — autonomous AI agents

Pro tip: Share this README with your AI assistant so it understands the full CLI syntax and can build sophisticated pipelines autonomously.

🔹 n8n (Execute Command Node)

// In n8n Execute Command node
C:\DDC\RvtExporter.exe "{{ $json.filePath }}" complete bbox

🔹 Python Subprocess

import subprocess

result = subprocess.run([
    r"C:\DDC\RvtExporter.exe",
    r"C:\Projects\Building.rvt",
    "complete", "bbox", "schedule"
], capture_output=True, text=True)

print(result.stdout)

🔹 Node.js / JavaScript

const { execSync } = require('child_process');

const output = execSync(
  'C:\\DDC\\RvtExporter.exe "C:\\Projects\\Building.rvt" complete bbox'
);
console.log(output.toString());

🔹 Make / Makefile

CONVERTER = C:/DDC/RvtExporter.exe

convert:
	$(CONVERTER) "$(INPUT)" complete bbox schedule

🔹 GitHub Actions / CI/CD

- name: Convert Revit to Excel
  run: |
    C:\DDC\RvtExporter.exe "${{ github.workspace }}\model.rvt" complete bbox

🔹 Docker (Windows Container)

COPY DDC_Converters_Windows_Packages/DDC_CONVERTER_Revit /app/DDC
RUN C:\app\DDC\RvtExporter.exe "C:\data\model.rvt"

🚀 AI Integration — Perfect Fuel for AI Products

Just clone the repo and describe what you want — AI does the rest

DDC converters are not just tools — they're ready-to-use fuel for AI-powered applications. Build cost estimation bots, automate construction workflows, or create intelligent assistants — the data works out of the box with modern AI tools.

Why DDC is Ideal for AI

Feature	Benefit
Structured output	Excel/JSON format that AI can analyze immediately
CLI interface	AI assistants can call converters directly
DDC CWICR integration	55,000+ work items with pre-computed embeddings for semantic search
Multi-format input	Revit, IFC, DWG, DGN — one interface for all formats

🛠️ Works Perfectly With

Claude Code

Google Antigravity

Cursor

Copilot

n8n

Dify

Windsurf

💻 Claude Code & Google Antigravity — AI Coding Assistants

The fastest way to work with DDC converters. Just open the repository and ask questions in natural language.

Getting Started:

# Clone the repository
git clone https://github.com/datadrivenconstruction/cad2data-Revit-IFC-DWG-DGN.git

# Open with Claude Code
cd cad2data-Revit-IFC-DWG-DGN
claude

Example Prompts:

Task	Prompt
Convert files	"Convert all .rvt files in C:\Projects to Excel with bounding boxes"
Analyze data	"Analyze the resulting XLSX and show all wall types with their volumes"
Build pipeline	"Create a Python script that converts Revit → parses Excel → generates cost report"
BIM validation	"Check BIM data quality and generate a parameter fill rate report"
Cost estimation	"Using DDC CWICR, estimate concrete work costs from this model"
CI/CD integration	"Write a GitHub Action that auto-converts .rvt files on push"

Pro Tips:

Point AI to specific files: "Analyze the Parquet file and summarize the cost distribution"
Ask for explanations: "Explain how the resource-based costing methodology works"
Request modifications: "Modify the n8n workflow to add email notifications"

📂 AI_AGENTS_INSTRUCTIONS Folder — Ready Context for AI Assistants

The repository includes a dedicated AI_AGENTS_INSTRUCTIONS folder containing everything AI coding assistants need to work effectively with these tools.

What's inside:

File	Purpose
INSTRUCTIONS.md	Main overview: repository philosophy, input/output formats, CLI examples
CLAUDE.md	Specific instructions for Claude Code with detailed CLI syntax
OPENCODE.md	Instructions for Opencode
ANTIGRAVITY.md	Instructions for Google Antigravity with GCP integration examples
TOOLS_OVERVIEW.md	Complete reference for all converters and process logic
DATA_DRIVEN_CONSTRUCTION_BOOK.txt	The "Data-Driven Construction" book — guiding philosophy for construction automation

Why this matters:

AI assistants can read these files to understand the full context
Contains CLI syntax, integration patterns, and best practices
The book serves as a "compass" for automation decisions in construction
n8n workflows are documented as visual process logic templates — not the final solution, but a foundation that can be implemented in any language (Python, JavaScript, C#, Go, Rust)

How to use:

# AI assistants automatically read AI_AGENTS_INSTRUCTIONS when working with the repo
# Or point them directly:
"Read AI_AGENTS_INSTRUCTIONS/CLAUDE.md and help me build a batch conversion pipeline"

🎯 DDC Skills — 196 Ready-to-Use AI Automations

New! DDC Skills for AI Agents in Construction — a complete automation toolkit for construction companies.

How It Works

1. Clone the Skills repository
2. Open with Claude Code, Cursor, or GitHub Copilot
3. Describe what you want to automate — AI guides you step by step

No coding required. The AI assistant reads the skill definitions and walks you through the entire automation process.

Skills for CAD/BIM Workflows

Category	Capabilities
BIM Processing	IFC parsing, Revit data extraction, DWG/DGN conversion
QTO Automation	Quantity takeoffs, material schedules, cost linking
Validation	Model checking, data quality reports, parameter fill rates
Reporting	Daily reports, photo reports, progress tracking
Cost Estimation	Automated estimates using DDC CWICR database
Integration	n8n workflows, Excel sync, API connections

Time Savings

Process	Reduction
Rate lookups	99% (15 min → 10 sec)
Daily reports	92%
Cost estimates	87%
Budget tracking	87%

The Skills repository combines this CAD2Data pipeline with the CWICR cost database — giving you end-to-end automation from model to estimate.

→ Explore 196 DDC Skills

Quick Start with n8n

Prerequisites

Install Node.js from nodejs.org.
Start n8n in Command Prompt:
```
npx n8n
```
Access at http://localhost:5678.
Download this repository from GitHub
- Click the green "Code" button → "Download ZIP"
- Unzip the folder
Run the Workflow
- You're ready. Just click Execute Workflow in n8n to start process your CAD-BIM files

⚡️ 1. Revit, IFC, DWG, DGN Basic Conversion

File: n8n_1_Revit_IFC_DWG_Conversation_simple.json

Converts CAD/BIM files (.rvt, .ifc, .dwg, .dgn) to Excel (XLSX) and Collada (DAE) for Revit/IFC files. Minimal configuration for quick setup.

Installation

Import n8n_1_Revit_IFC_DWG_Conversation_simple.json into n8n via Workflows > Import from File.

Update Set Variables node:

# Revit
path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
path_project_file: C:\Projects\Model.rvt

# Revit to IFC
path_to_converter: C:\Converters\datadrivenlibs\RVT2IFCconverter.exe
path_project_file: C:\Projects\Model.rvt

# IFC
path_to_converter: C:\Converters\datadrivenlibs\IfcExporter.exe
path_project_file: C:\Projects\Model.ifc

# DWG
path_to_converter: C:\Converters\datadrivenlibs\DwgExporter.exe
path_project_file: C:\Projects\Plan.dwg

# DGN
path_to_converter: C:\Converters\datadrivenlibs\DgnExporter.exe
path_project_file: C:\Projects\Bridge.dgn

Ensure the converter is in the datadrivenlibs folder, e.g., C:\Converters\datadrivenlibs\XxxExporter.exe.

Usage

Run the workflow via Manual Trigger.
Check the output folder for XLSX, DAE, and PDF files.
Monitor logs for conversion status.

graph LR;
    A[Manual Trigger] --> B[Set Variables];
    B --> C[Execute Pipeline];
    C --> D[Output XLSX + DAE + PDF];

⚡️ 2. Revit Conversion with Advanced Settings

File: n8n_2_All_Settings_Revit_IFC_DWG_Conversation_simple.json

Converts CAD/BIM files with customizable export modes (basic: 309 categories, standard: 724 categories, complete: all 1209 categories) and optional outputs like bounding box, Revit schedules, or PDF drawings.

Installation

Import n8n_2_All_Settings_Revit_IFC_DWG_Conversation_simple.json into n8n via Workflows > Import from File.
Update Set Variables node with converter and file paths (same as Basic Conversion).

Configure export options:

export_mode: basic | standard | complete
bbox: true | false
schedule: true | false
sheets2pdf: true | false
no-xlsx: true | false
no-collada: true | false

Usage

Run the workflow via Manual Trigger.
Check the output folder for XLSX, DAE, schedules, or PDF files based on settings.
Monitor logs for conversion status.

graph LR;
    A[Manual Trigger] --> B[Set Variables];
    B --> C[Execute Pipeline];
    C --> D{Export Options};
    D -->|Standard| F[XLSX + DAE];
    D -->|+BBox| G[XLSX + DAE + BBox];
    D -->|+Schedules| H[XLSX + DAE + Schedules];
    D -->|+PDF| I[XLSX + DAE + PDF];

⚡️ 3. Revit, IFC, DWG Batch Conversion with Validation and Reporting

File: n8n_3_CAD-BIM-Batch-Converter-Pipeline.json

Automates batch conversion of Revit (.rvt) files to Excel (XLSX) and Collada (DAE), validates outputs, tracks processing times, and generates an HTML report with metrics, file links, and configuration details.

Installation

Import n8n_3_CAD-BIM-Batch-Converter-Pipeline.json into n8n via Workflows > Import from File.

Update Set Configuration Parameters node:

converter_path: C:\Converters\datadrivenlibs\RvtExporter.exe
source_folder: C:\Sample_Projects
output_folder: C:\Output
include_subfolders: true
file_extension: .rvt

Ensure RvtExporter.exe is in C:\Converters\datadrivenlibs\ and .rvt files are in the source folder.

Usage

Run the workflow via Manual Trigger.
Monitor logs for file discovery and conversion progress.
Review the HTML report (auto-opens in browser) with:
- Metrics (files processed, success rate, time, sizes).
- Success/failure tables with file links.
Check the output folder for XLSX and DAE files.

graph LR;
    A[Manual Trigger] --> B[Set Config];
    B --> C[Scan Files];
    C --> D[Batch Convert];
    D --> E[Validate Outputs];
    E --> F[Track Metrics];
    F --> G[Generate HTML Report];
    G --> H[Save & Open Report];

⚡️ 4. Multi-Format CAD (BIM) Validation for Revit, IFC, DWG, DGN

Files: n8n_4_Validation_CAD_BIM_Revit_IFC_DWG.json, DDC_BIM_Requirements_Table_for_Revit_IFC_DWG.xlsx

Validates CAD/BIM data against predefined rules, generating color-coded Excel reports with data quality metrics.

Installation

Import n8n_3_Validation_CAD_BIM_Revit_IFC_DWG.json into n8n via Workflows > Import from File.

Update Setup Paths node:

path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
project_file: C:\Projects\Model.rvt
validation_rules_path: C:\Validation\DDC_Revit_IFC_Validation_Table.xlsx

Ensure the converter and validation rules file are accessible.

Usage

Run the workflow via Manual Trigger.
Check the output folder for the color-coded XLSX report.
Review data quality metrics (fill rates, unique values, patterns).
Monitor logs for validation status.

graph LR;
    A[Manual Trigger] --> B[Setup Paths];
    B --> C{File Exists?};
    C -->|No| D[Convert to Structured];
    C -->|Yes| E[Load Data];
    D --> E;
    E --> F[Load Rules];
    F --> G[Validate Data];
    G --> H[Calculate Metrics];
    H --> I[Generate Report];
    I --> J[Save & Open];

⚡️ 5. Universal BIM/CAD Classification with AI & RAG for Revit, IFC, DWG, DGN

File: n8n_5_CAD_BIM_Automatic_Classification_with_LLM_and_RAG.json

🔗 Enhanced with DDC CWICR Database: OpenConstructionEstimate-DDC-CWICR
This workflow leverages the DDC CWICR vector database (Qdrant) containing 55,719 work items with pre-computed OpenAI embeddings (3072d). The RAG pipeline performs semantic search across 9 languages, matching BIM elements to standardized construction work descriptions. The database covers the full spectrum of construction activities — from earthworks and concrete to specialized MEP installations — enabling accurate classification against any standard (Omniclass, Uniclass, MasterFormat, or custom systems).

Intelligently classifies building elements from CAD/BIM files using AI and ANY classification system - international standards (Omniclass, Uniclass, etc.) or your custom/proprietary classifications. Supports automatic dictionary extraction from mapping files.

Key Features

Universal Classification: Works with ANY classification system - standard or custom
AI-Powered Classification: Uses LLMs to classify elements with confidence scoring
Smart Mapping: Automatically extracts dictionaries from Excel, CSV, PDF files
Automatic Filtering: Separates building elements from drawings/annotations
Hierarchical Support: Handles both flat and hierarchical classification structures
Professional Reports: Interactive HTML dashboards + multi-sheet Excel
RAG Technology: Retrieval-Augmented Generation for accurate classification

Installation

Import n8n_5_CAD_BIM_Automatic_Classification_with_LLM_and_RAG.json into n8n
Configure AI credentials (OpenAI/Anthropic/OpenRouter/Gemini/xAI)

Update Setup - Define file paths node:

path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
project_file: C:\Projects\Model.rvt
group_by: Type Name
classification_name: [Any classification name]
optional_mapping_file: C:\Classifications\[your_classification].xlsx
optional_help_prompt: "Additional context for AI"

Classification Flexibility

This pipeline works with ANY classification system:

✅ International standards (Omniclass, Uniclass, MasterFormat, etc.)
✅ National standards (DIN, NF, BS, etc.)
✅ Company-specific classifications
✅ Custom project classifications
✅ Proprietary coding systems
✅ Any structured classification in Excel/CSV/PDF format

How It Works

With Mapping File: Provide your classification dictionary (Excel/CSV/PDF) - the AI will extract codes and apply them accurately
Without Mapping File: AI uses its knowledge to classify according to the standard you specify
Hybrid Mode: Combine mapping file with AI intelligence for best results

⏱️ Processing Time: 3-10 seconds per element group (varies by LLM model)

graph LR;
    A[CAD/BIM File] --> B[Convert to Excel];
    B --> C[Filter Elements];
    C --> D{Mapping File?};
    D -->|Yes| E[Extract Dictionary];
    D -->|No| F[Direct AI Classification];
    E --> G[AI Classification with RAG];
    F --> G;
    G --> H[Confidence Scoring];
    H --> I[Professional Reports];

⚡️ 6. Construction Cost Estimation Pipelines

🔗 Powered by DDC CWICR Database: OpenConstructionEstimate-DDC-CWICR

The cost estimation workflows connect to the DDC CWICR cost database containing 55,719 work items and 27,672 resources with detailed price breakdowns across 10+ regional markets. The resource-based methodology separates physical norms (labor hours, material quantities, equipment time) from volatile prices, ensuring transparent and auditable estimates.

📦 Database Downloads: DDC CWICR Releases — Excel, Parquet, CSV, Qdrant snapshots
🌐 Live Demo: openconstructionestimate.com — explore the database and semantic search

⚡️ 6.1 Construction Price Estimation Pipeline for Revit and IFC with LLM (AI)

File: n8n_6_Construction_Price_Estimation_Pipeline.json

Automates cost estimation for building elements from CAD/BIM files. Uses AI to classify materials, search market prices, and generate comprehensive cost reports.

Key Features

AI Classification: Materials across EU/DE/US standards
Smart Pricing: Region-specific databases with fallbacks
Cost Analysis: Total costs, cost per unit, top 10 groups
Multi-Format Output: Excel workbook + HTML report with charts

Installation

Import Construction_Price_Estimation_Pipeline.json into n8n
Configure AI credentials (OpenAI/Anthropic)

Update Set Parameters node:

input_file_path: C:\Output\Project_Elements.xlsx
grouping_parameter: Type Name )
country: Germany

Grouping parameter (group_by, e.g. 'Type Name', 'IfcType' for IFC or other)
Country (country for which the values will be calculated, e.g. 'Germany'or 'Brazil')

⏱️ Processing Time: 5-15 seconds per element group (depends on LLM speed)

graph LR;
    A[CAD/BIM Excel] --> B[Group Elements];
    B --> C[AI Classification];
    C --> D[Price Search];
    D --> E[Cost Calculation];
    E --> F[Reports: Excel + HTML];

⚡️ 6.2 CAD (BIM) Cost Estimation Pipeline 4D/5D with DDC CWICR

File: n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json

🔗 Workflow Repository: OpenConstructionEstimate-DDC-CWICR

Automated cost estimation from Revit/IFC/DWG models. Extracts BIM data, classifies elements, decomposes into work items, and generates 4D/5D estimates with full resource breakdown.

Pipeline Stages

Stage	Name	Description
0	Collect BIM Data	Extract elements from Revit via DDC Converter
1	Project Detection	AI identifies project type (Residential, Commercial, etc.)
2	Phase Generation	AI creates construction phases
3	Element Assignment	AI maps BIM types to phases
4	Work Decomposition	AI breaks types into work items ("Brick Wall" → masonry, mortar)
5	Vector Search	Find matching rates in DDC CWICR via Qdrant
6	Unit Mapping	Convert BIM units to rate units
7	Cost Calculation	Qty × Unit Price for each work item
7.5	Validation	CTO review for completeness and duplicates
8	Aggregation	Sum by phases and categories
9	Report Generation	Create HTML and Excel outputs

flowchart TB
    subgraph INPUT["📁 INPUT<br/><i>CAD • photos • text description</i>"]
        CAD["📐 Project Input<br/>(text • photos • RVT / IFC / DWG)"]
    end

    subgraph EXTRACT["⚙️ EXTRACTION"]
        CONV["RvtExporter.exe / CAD Export  / ETL"]
        XLSX["📊 .XLSX<br/>(Raw Elements)"]
    end

    subgraph PREP["🔧 DATA PREPARATION"]
        PREP_AI["🤖 AI: Clean & Classify<br/><i>headers • types • categories</i>"]
    end

    subgraph STAGE_PLAN["📋 STAGES 1–3: Planning"]
        PLAN["🤖 Detect Project & Phases<br/><i>new / renovation / demolition</i><br/><i>small / medium / large</i><br/><i>elements → construction phases</i>"]
    end

    subgraph STAGE4["🔨 STAGE 4: Decomposition"]
        S4["🤖 Decompose Types to Works<br/><i>'Brick Wall 240mm' → masonry, mortar, plaster</i>"]
    end

    subgraph STAGE5["💰 STAGE 5: Pricing"]
        S5["🤖 Price via Vector DB<br/><i>OpenAI embeddings + Qdrant</i><br/><i>rate_code, unit_cost, resources</i>"]
    end

    subgraph STAGE75["✅ STAGE 7.5: Validation"]
        S75["🤖 CTO Review<br/><i>completeness • duplicates • missing works</i>"]
    end

    subgraph OUTPUT["📤 OUTPUT"]
        HTML["📄 HTML Report"]
        XLS["📊 XLS Report"]
    end

    CAD --> CONV --> XLSX
    XLSX --> PREP_AI --> PLAN --> S4 --> S5 --> S75
    S75 --> HTML & XLS

    style INPUT fill:#f4f4f5,stroke:#d4d4d8,color:#18181b
    style EXTRACT fill:#e0f2fe,stroke:#bae6fd,color:#0f172a
    style PREP fill:#ede9fe,stroke:#ddd6fe,color:#1e1b4b
    style STAGE_PLAN fill:#ecfdf5,stroke:#bbf7d0,color:#064e3b
    style STAGE4 fill:#fef9c3,stroke:#fef3c7,color:#78350f
    style STAGE5 fill:#fee2e2,stroke:#fecaca,color:#7f1d1d
    style STAGE75 fill:#e0f2f1,stroke:#bae5e1,color:#134e4a
    style OUTPUT fill:#eef2ff,stroke:#e0e7ff,color:#111827

Key Features

Full BIM Integration: Native support for Revit, IFC, DWG via DDC Converter
AI-Powered Decomposition: Breaks complex BIM types into constituent work items
Semantic Pricing: Qdrant vector search with 55,719 pre-embedded work items
Multi-LLM Support: OpenAI GPT-4o, Claude, Gemini 2.5 Pro, xAI Grok, DeepSeek
CTO Validation: AI review stage checks completeness and catches duplicates
9 Languages: AR, DE, EN, ES, FR, HI, PT, RU, ZH with regional pricing

Prerequisites

Component	Requirement	Description
n8n	v1.0+ (self-hosted)	Workflow automation platform
Qdrant	Cloud or self-hosted	Vector database for semantic search
OpenAI API	`text-embedding-3-large`	Generates embeddings for matching
LLM API	OpenAI / Claude / Gemini / Grok	AI models for classification
DDC Converter	`RvtExporter.exe`	Extracts BIM data to Excel

Supported Languages & Price Levels

Code	Language	Price Level	Currency	Qdrant Collection
`AR`	Arabic	Dubai	AED	`ddc_cwicr_ar`
`DE`	German	Berlin	EUR	`ddc_cwicr_de`
`EN`	English	Toronto	CAD	`ddc_cwicr_en`
`ES`	Spanish	Barcelona	EUR	`ddc_cwicr_es`
`FR`	French	Paris	EUR	`ddc_cwicr_fr`
`HI`	Hindi	Mumbai	INR	`ddc_cwicr_hi`
`PT`	Portuguese	São Paulo	BRL	`ddc_cwicr_pt`
`RU`	Russian	St. Petersburg	RUB	`ddc_cwicr_ru`
`ZH`	Chinese	Shanghai	CNY	`ddc_cwicr_zh`

Output Files

Reports are saved to the project folder:

project_YYYY-MM-DD.html   ← Interactive report (opens in browser)
project_YYYY-MM-DD.xls    ← Excel-compatible spreadsheet

LLM Model Selection

The workflow supports multiple AI providers. Enable your preferred model:

Model	Status
OpenAI GPT-4o	✅ Default
Claude Opus 4	Disabled
Gemini 2.5 Pro	Disabled
xAI Grok	Disabled
DeepSeek	Disabled

To switch models: Enable the desired model node and Disable others.

⏱️ Processing Time: Varies by project size and LLM model

⚡️ 7. Carbon Footprint CO2 Estimator for Revit and IFC with LLM (AI)

File: n8n_7_Carbon_Footprint_CO2_Estimator_for_Revit_and_IFC.json

🔗 Integrated with DDC CWICR Database: OpenConstructionEstimate-DDC-CWICR
This workflow utilizes DDC CWICR's detailed material classifications and resource decomposition to calculate embodied carbon (A1-A3 lifecycle stages). The database provides precise material quantities per work item — concrete volumes, steel tonnages, insulation areas — which are then matched with CO₂e emission factors. With data covering 9 languages and multiple regional standards (EU/DE/US), the pipeline delivers accurate sustainability assessments for international projects.

Calculates embodied carbon emissions for building projects. Analyzes materials, applies emission factors, and generates professional sustainability reports.

Key Features

Embodied Carbon Analysis: A1-A3 lifecycle stages
Material Classification: EU/DE/US standards with density data
Emission Factors: Industry-standard CO2e factors per material
Impact Assessment: Critical/High/Medium/Low categorization
Professional Reports: McKinsey-style HTML + Multi-sheet Excel

Installation

Import n8n_6_Carbon_Footprint_CO2_Estimator_for_Revit_and_IFC.json into n8n
Configure AI credentials (OpenAI/Anthropic)

Update Setup - Define file paths node:

path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
project_file: C:\Projects\Model.rvt
group_by: Type Name (Category or other)
country: Germany (country for which the values will be calculated, e.g. 'Germany'or 'Brazil')

⏱️ Processing Time: 5-15 seconds per element group (depends on LLM speed)

graph LR;
    A[Revit/IFC File] --> B[Convert to Excel];
    B --> C[Group Elements];
    C --> D[AI Material Analysis];
    D --> E[CO2 Calculation];
    E --> F[Generate Reports];
    F --> G[Excel + HTML Output];

⚡️ 8. Simple ETL for LLM Use Cases for Revit, IFC, DWG, DGN

File: n8n_8_Revit_IFC_DWG_Conversation_EXTRACT_Phase_with_Parse_XLSX.json

Converts a Revit file to Excel, generates an XLSX filename, and parses data for LLM-based automation tasks.

Installation

Import n8n_4_Revit_IFC_DWG_Conversation_EXTRACT_Phase_with_Parse_XLSX.json into n8n via Workflows > Import from File.

Update Setup Paths node:

path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
project_file: C:\Projects\Model.rvt

Ensure the converter is accessible.

Usage

Run the workflow via Manual Trigger.
Check the output folder for the XLSX file.
Use the parsed data for LLM tasks (e.g., feed JSON to Claude or ChatGPT).
Monitor logs for conversion and parsing status.

⚡️ 9. Revit and IFC to HTML Quantity Takeoff

File: n8n_9_CAD_BIM_Quantity_TakeOff_HTML_Report_Generatorn.json

Analyzes Revit wall data, calculates volumes by type, and generates interactive HTML reports with summary statistics.

Installation

Import n8n_5_CAD_BIM_Quantity_TakeOff_HTML_Report_Generatorn.json into n8n via Workflows > Import from File.

Update Setup Paths node:

path_to_converter: C:\Converters\datadrivenlibs\RvtExporter.exe
project_file: C:\Projects\Model.rvt

Ensure the converter is accessible.

Usage

Run the workflow via Manual Trigger.
Check the output folder for the HTML report.
Review the report (auto-opens in browser) for wall quantities and statistics.
Monitor logs for processing status.

graph LR;
    A[Manual Trigger] --> B[Setup Paths];
    B --> C[Run Converter];
    C --> D{Success?};
    D -->|No| E[Error Message];
    D -->|Yes| F[Read Excel];
    F --> G[Parse Data];
    G --> H[Filter Walls];
    H --> I[Clean Data];
    I --> J[Group & Sum];
    J --> K[Generate HTML];
    K --> L[Save Report];
    L --> M[Success];

Troubleshooting

VC++ Runtime Missing (v17 and later)

Starting with v17, the converters use an updated runtime baseline. Please install Microsoft Visual C++ Redistributable 2015–2022 (x64) before running any .exe tools (RvtExporter.exe, RVT2IFCconverter.exe, etc.).

Symptoms:

Converters fail to start
Error messages about missing DLLs
Common on fresh Windows installs or VMs

Solution:

Download from Microsoft Learn: Latest supported VC++ 2015–2022
Choose the x64 package
Run VC_redist.x64.exe

Why you need this: v17 switched the technical baseline from v16; the VC++ runtime is a required dependency now.

Execute Command Node Missing (n8n 2.0+)

Symptoms:

Nodes show with question mark (?)
Error: Unrecognized node type: n8n-nodes-base.executeCommand
Execute Command doesn't appear in node search

Solution: Add environment variable before starting n8n:

set NODES_EXCLUDE=[] && npx n8n

Or create .env file in C:\Users\YOUR_USER\.n8n\.env with NODES_EXCLUDE=[]

See ⚠️ n8n 2.0+ Setup for details.

Module 'os' Blocked Error

In n8n versions 1.98.0–1.101.x, the os module is blocked, affecting libraries like pandas. Solution: Use the latest version with npx n8n@latest.

What is DataFrames?

CAD/BIM formats like .rvt, .ifc, .dwg, or .dgn are complex and proprietary. Converting them into DataFrames—tabular structures with rows (elements) and columns (properties)—enables efficient data processing. Popularized by Python’s pandas library, DataFrames are widely used for their compatibility with automation, analytics, and AI tools (only one of Python's libraries, pandas, is downloaded 12 million times a day). They simplify tasks like filtering, grouping, and visualization, making them ideal for dashboards, quantity takeoffs, and validation.

Back to the Roots of “BIM”. 𝗧𝗵𝗲 𝗟𝗼𝘀𝘁 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆: 𝗳𝗿𝗼𝗺 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝘁𝗼 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗕𝘂𝘇𝘇𝘄𝗼𝗿𝗱. At the beginning, BIM was never about buzzwords or endless interoperability debates. Its foundation was always databases.

🔹 1974. Charles Eastman introduced the Building Description System (BDS). In his paper, the word database appeared 43 times. 🔹 2000. ADSK published a whitepaper stressing the value of direct access to the “CAD database.” Neutral translators like STEP/IFC were considered secondary. 💬 “Native data exchange capability – applications should access the main CAD database directly, so detail and accuracy are not lost.” 🔹 2002. After acquiring Revit-BOM, ADSK’s BIM whitepaper again placed the database at the core (23 mentions of the term). 🔹 2003. For the last time, ADSK officially tied BIM to IT and databases. After that, the database vanished from the narrative — replaced by pure marketing.

In reality, BIM has always been simple: a database of project elements, each with its own parameters. Everything else is marketing layers. Maybe it’s time to go back to the essence: open, structured, and accessible data.

Learn More:

Excel to Revit Update Project from Excel

After transforming and enriching your Excel data, you can effortlessly push the modified data back into your Revit project. Our dedicated tool ImportExcelToRevit makes this process seamless by directly importing updated Excel sheets into Revit parameters and families.

Simplify your BIM workflow: Revit ➡️ Excel ➡️ Transform ➡️ Excel ➡️ Revit.

🗃️ DDC CWICR - Construction Cost Database

For the highest-quality construction cost estimation, this repository integrates with the OpenConstructionEstimate-DDC-CWICR — an open multilingual construction cost database.

Why DDC CWICR?

DDC CWICR (Construction Work Items, Components & Resources) provides the foundation for accurate, transparent, and auditable cost estimation:

55,719 Work Items — comprehensive coverage of construction activities
27,672 Resources — materials, labor, and equipment with detailed breakdowns
9 Languages — Arabic, Chinese, German, English, Spanish, French, Hindi, Portuguese, Russian
85 Data Fields — full resource-based cost structure per work item
Semantic Search — Qdrant vector database with OpenAI embeddings (3072d) for natural language queries

Database Advantages

Feature	Benefit
Resource-Based Methodology	Physical norms (labor hours, material quantities) separated from volatile prices
Full Transparency	Complete breakdown of every cost component — no hidden markups
Multi-Format Export	Excel, Parquet, CSV, Qdrant snapshots for any integration scenario
AI-Ready	Pre-computed embeddings enable RAG pipelines and LLM-powered estimation

Live Demo & Resources

🌐 Live Demo: openconstructionestimate.com — explore the database and semantic search in action

📦 Repository: github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR

The workflows in this repository (especially Workflow 5, 6, and 7) leverage DDC CWICR for classification, pricing, and carbon footprint calculations, ensuring professional-grade estimation quality.

Contributing

We welcome contributions! Please feel free to:

Report bugs
Suggest features
Submit pull requests
Improve documentation

Support

🌐 Website: DataDrivenConstruction.io 💬 Issues: GitHub Issues 📧 Email: info@datadrivenconstruction.io

Consulting and Training

We work with leading construction, engineering, consulting agencies and technology firms around the world to help them implement open data principles, automate CAD/BIM processing and build robust ETL pipelines.

If you would like to test this solution with your own data, or are interested in adapting the workflow to real project tasks, feel free to contact us.

Our team delivers hands-on workshops, provides strategic consulting, and develops prototypes tailored to real project processes. We actively support organizations seeking practical solutions for digital transformation and interoperability, focusing on data quality and classification challenges, and driving the adoption of open and automated workflows.

Contact us for a free consultation where we'll discuss your challenges and demonstrate how n8n automation can transform your operations. Reach out via Email at [@DataDrivenConstruction](mailto: info@datadrivenconstruction.io) or visit our website at datadrivenconstruction.io to learn more about our services.

Unlock the Power of Data in Construction
🚀 Move to full-cycle data management where only unified
structured data & processes remain and where 🔓 your data is yours

Trademarks

Autodesk®, Revit®, AutoCAD®, and DWG™ are registered trademarks or trademarks of Autodesk, Inc. MicroStation® and DGN™ are trademarks of Bentley Systems, Incorporated. IFC is a trademark of buildingSMART International Ltd. OmniClass® and MasterFormat® are trademarks of The Construction Specifications Institute (CSI). All other brand names, product names, or trademarks belong to their respective holders.

This project is not affiliated with, endorsed by, or sponsored by Autodesk, Bentley Systems, buildingSMART, or any other trademark holders mentioned above.

Name		Name	Last commit message	Last commit date
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.github		.github
AI_AGENTS_INSTRUCTIONS		AI_AGENTS_INSTRUCTIONS
DDC_LINUX_Converters		DDC_LINUX_Converters
DDC_WINDOWS_Converters		DDC_WINDOWS_Converters
DDC_in_additon		DDC_in_additon
DDC_n8n_Workflows&Pipelines		DDC_n8n_Workflows&Pipelines
Sample_Projects		Sample_Projects
.gitignore		.gitignore
GuideBook_DataDrivenConstruction_Book_2ndEdition_ArtemBoiko_2025_en-US.pdf		GuideBook_DataDrivenConstruction_Book_2ndEdition_ArtemBoiko_2025_en-US.pdf
LICENSE		LICENSE
README.ar.md		README.ar.md
README.de.md		README.de.md
README.es.md		README.es.md
README.fr.md		README.fr.md
README.md		README.md
README.ru.md		README.ru.md
README.zh.md		README.zh.md

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CAD/BIM (Revit, DWG, IFC, DGN) processing and conversion with batch handling, grouping, checks, cost estimation and QTO reports. Visualization of automation processes in open agents and workflows

Table of Contents

Tutorial Videos

Overview

Supported Formats

Key Features

Running the Converters

🖥️ Command Line Interface (CLI)

🤖 Why CLI Matters: Let AI Build Your Pipelines

RvtExporter.exe — Revit to XLSX/DAE/PDF

RVT2IFCconverter.exe — Revit to IFC

Integration Examples

🔹 PowerShell / Batch Scripts

🔹 VS Code Tasks

🔹 AI Coding Assistants (Claude Code, Cursor, Copilot, Windsurf, Aider, Cline)

🔹 n8n (Execute Command Node)

🔹 Python Subprocess

🔹 Node.js / JavaScript

🔹 Make / Makefile

🔹 GitHub Actions / CI/CD

🔹 Docker (Windows Container)

🚀 AI Integration — Perfect Fuel for AI Products

Why DDC is Ideal for AI

🛠️ Works Perfectly With

💻 Claude Code & Google Antigravity — AI Coding Assistants

📂 AI_AGENTS_INSTRUCTIONS Folder — Ready Context for AI Assistants

🎯 DDC Skills — 196 Ready-to-Use AI Automations

How It Works

Skills for CAD/BIM Workflows

Time Savings

Quick Start with n8n

Prerequisites

⚡️ 1. Revit, IFC, DWG, DGN Basic Conversion

Installation

Usage

⚡️ 2. Revit Conversion with Advanced Settings

Installation

Usage

⚡️ 3. Revit, IFC, DWG Batch Conversion with Validation and Reporting

Installation

Usage

⚡️ 4. Multi-Format CAD (BIM) Validation for Revit, IFC, DWG, DGN

Installation

Usage

⚡️ 5. Universal BIM/CAD Classification with AI & RAG for Revit, IFC, DWG, DGN

Key Features

Installation

Classification Flexibility

How It Works

⚡️ 6. Construction Cost Estimation Pipelines

⚡️ 6.1 Construction Price Estimation Pipeline for Revit and IFC with LLM (AI)

Key Features

Installation

⚡️ 6.2 CAD (BIM) Cost Estimation Pipeline 4D/5D with DDC CWICR

Pipeline Stages

Key Features

Prerequisites

Supported Languages & Price Levels

Output Files

LLM Model Selection

⚡️ 7. Carbon Footprint CO2 Estimator for Revit and IFC with LLM (AI)

Key Features

Installation

⚡️ 8. Simple ETL for LLM Use Cases for Revit, IFC, DWG, DGN

Installation

Usage

⚡️ 9. Revit and IFC to HTML Quantity Takeoff

Installation

Usage

Troubleshooting

VC++ Runtime Missing (v17 and later)

Execute Command Node Missing (n8n 2.0+)

Module 'os' Blocked Error

What is DataFrames?

Excel to Revit Update Project from Excel