dgn-to-excel
This Claude Code skill converts DGN files (versions 7, 8, and 8i) from MicroStation infrastructure CAD software into structured Excel databases. Use it when you need to extract and analyze geometric elements, levels, text, and properties from civil engineering drawings such as transportation, bridge, utility, or rail infrastructure projects for reporting and data analysis purposes.
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction /tmp/dgn-to-excel && cp -r /tmp/dgn-to-excel/1_DDC_Toolkit/CAD-Converters/dgn-to-excel ~/.claude/skills/dgn-to-excelSKILL.md
# DGN to Excel Conversion
## Business Case
### Problem Statement
DGN files are common in infrastructure and civil engineering:
- Transportation and highway design
- Bridge and tunnel projects
- Utility networks
- Rail infrastructure
Extracting structured data from DGN files for analysis and reporting can be challenging.
### Solution
Convert DGN files to structured Excel databases, supporting both v7 and v8 formats.
### Business Value
- **Infrastructure support** - Civil engineering focused
- **Legacy format support** - V7 and V8 DGN files
- **Data extraction** - Levels, cells, text, geometry
- **Batch processing** - Process multiple files
- **Structured output** - Excel format for analysis
## Technical Implementation
### CLI Syntax
```bash
DgnExporter.exe <input_dgn>
```
### Supported Versions
| Version | Description |
|---------|-------------|
| V7 DGN | Legacy MicroStation format (pre-V8) |
| V8 DGN | Modern MicroStation format |
| V8i DGN | MicroStation V8i format |
### Output Format
| Output | Description |
|--------|-------------|
| `.xlsx` | Excel database with all elements |
### Examples
```bash
# Basic conversion
DgnExporter.exe "C:\Projects\Bridge.dgn"
# Batch processing
for /R "C:\Infrastructure" %f in (*.dgn) do DgnExporter.exe "%f"
# PowerShell batch
Get-ChildItem "C:\Projects\*.dgn" -Recurse | ForEach-Object {
& "C:\DDC\DgnExporter.exe" $_.FullName
}
```
### Python Integration
```python
import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional, Dict, Any
from dataclasses import dataclass
from enum import Enum
class DGNElementType(Enum):
"""DGN element types."""
CELL_HEADER = 2
LINE = 3
LINE_STRING = 4
SHAPE = 6
TEXT_NODE = 7
CURVE = 11
COMPLEX_CHAIN = 12
COMPLEX_SHAPE = 14
ELLIPSE = 15
ARC = 16
TEXT = 17
SURFACE = 18
SOLID = 19
BSPLINE_CURVE = 21
POINT_STRING = 22
DIMENSION = 33
SHARED_CELL = 35
@dataclass
class DGNElement:
"""Represents a DGN element."""
element_id: int
element_type: int
type_name: str
level: int
color: int
weight: int
style: int
# Geometry
range_low_x: Optional[float] = None
range_low_y: Optional[float] = None
range_low_z: Optional[float] = None
range_high_x: Optional[float] = None
range_high_y: Optional[float] = None
range_high_z: Optional[float] = None
# Cell/Text specific
cell_name: Optional[str] = None
text_content: Optional[str] = None
@dataclass
class DGNLevel:
"""Represents a DGN level."""
number: int
name: str
is_displayed: bool
is_frozen: bool
element_count: int
class DGNExporter:
"""DGN to Excel converter using DDC DgnExporter CLI."""
def __init__(self, exporter_path: str = "DgnExporter.exe"):
self.exporter = Path(exporter_path)
if not self.exporter.exists():
raise FileNotFoundError(f"DgnExporter not found: {exporter_path}")
def convert(self, dgn_file: str) -> Path:
"""Convert DGN file to Excel."""
dgn_path = Path(dgn_file)
if not dgn_path.exists():
raise FileNotFoundError(f"DGN file not found: {dgn_file}")
cmd = [str(self.exporter), str(dgn_path)]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"Export failed: {result.stderr}")
return dgn_path.with_suffix('.xlsx')
def batch_convert(self, folder: str,
include_subfolders: bool = True) -> List[Dict[str, Any]]:
"""Convert all DGN files in folder."""
folder_path = Path(folder)
pattern = "**/*.dgn" if include_subfolders else "*.dgn"
results = []
for dgn_file in folder_path.glob(pattern):
try:
output = self.convert(str(dgn_file))
results.append({
'input': str(dgn_file),
'output': str(output),
'status': 'success'
})
print(f"✓ Converted: {dgn_file.name}")
except Exception as e:
results.append({
'input': str(dgn_file),
'output': None,
'status': 'failed',
'error': str(e)
})
print(f"✗ Failed: {dgn_file.name} - {e}")
return results
def read_elements(self, xlsx_file: str) -> pd.DataFrame:
"""Read converted Excel as DataFrame."""
return pd.read_excel(xlsx_file, sheet_name="Elements")
def get_levels(self, xlsx_file: str) -> pd.DataFrame:
"""Get level summary."""
df = self.read_elements(xlsx_file)
if 'Level' not in df.columns:
raise ValueError("Level column not found")
summary = df.groupby('Level').agg({
'ElementId': 'count'
}).reset_index()
summary.columns = ['Level', 'Element_Count']
return summary.sort_values('Level')
def get_element_types(self, xlsx_file: str) -> pd.DataFrame:
"""Get element type statistics."""
df = self.read_elements(xlsx_file)
type_col = 'ElementType' if 'ElementType' in df.columns else 'Type'
if type_col not in df.columns:
return pd.DataFrame()
summary = df.groupby(type_col).agg({
'ElementId': 'count'
}).reset_index()
summary.columns = ['Element_Type', 'Count']
return summary.sort_values('Count', ascending=False)
def get_cells(self, xlsx_file: str) -> pd.DataFrame:
"""Get cell references (similar to blocks in DWG)."""
df = self.read_elements(xlsx_file)
# Filter to cell elements
cells = df[df['ElementType'].isin([2, 35])] # CELL_HEADER, SHARED_CELL
if cells.empty or 'CellName' not in cells.columns:
return pd.DataFrame(columns=['Cell_Name', 'Count'])
summary = celAnalyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
Manage as-built documentation for project closeout. Track drawing markups, coordinate updates, and verify completeness.
Track as-built documentation and record drawings. Monitor submission status, manage revisions, and ensure completeness for handover.
Batch convert multiple CAD/BIM files (Revit, IFC, DWG, DGN) with progress tracking, error handling, and consolidated reporting.
Compare and analyze contractor bids. Score proposals, identify scope gaps, and recommend selections.
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
Automated cost estimation from BIM models using DDC CWICR database (8 national bases, 78,228 positions). AI classification + vector search for accurate pricing.