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

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

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

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 = cells.groupby('CellName').agg({
        'ElementId': 'count'
    }).reset_index()
    summary.columns = ['Cell_Name', 'Count']
    return summary.sort_values('Count', ascending=False)

def get_text_content(self, xlsx_file: str) -> pd.DataFrame:
    """Extract all text from DGN."""
    df = self.read_elements(xlsx_file)

    # Filter to text elements
    text_types = [7, 17]  # TEXT_NODE, TEXT
    texts = df[df['ElementType'].isin(text_types)]

    if 'TextContent' in texts.columns:
        return texts[['ElementId', 'Level', 'TextContent']].copy()
    return texts[['ElementId', 'Level']].copy()

def get_statistics(self, xlsx_file: str) -> Dict[str, Any]:
    """Get comprehensive DGN statistics."""
    df = self.read_elements(xlsx_file)

    stats = {
        'total_elements': len(df),
        'levels_used': df['Level'].nunique() if 'Level' in df.columns else 0,
        'element_types': df['ElementType'].nunique() if 'ElementType' in df.columns else 0
    }

    # Calculate extents
    for coord in ['X', 'Y', 'Z']:
        low_col = f'RangeLow{coord}'
        high_col = f'RangeHigh{coord}'
        if low_col in df.columns and high_col in df.columns:
            stats[f'min_{coord.lower()}'] = df[low_col].min()
            stats[f'max_{coord.lower()}'] = df[high_col].max()

    return stats

class DGNAnalyzer: """Advanced DGN analysis for infrastructure projects."""

def __init__(self, exporter: DGNExporter):
    self.exporter = exporter

def analyze_infrastructure(self, dgn_file: str) -> Dict[str, Any]:
    """Analyze DGN for infrastructure elements."""
    xlsx = self.exporter.convert(dgn_file)
    df = self.exporter.read_elements(str(xlsx))

    analysis = {
        'file': dgn_file,
        'statistics': self.exporter.get_statistics(str(xlsx)),
        'levels': self.exporter.get_levels(str(xlsx)).to_dict('records'),
        'element_types': self.exporter.get_element_types(str(xlsx)).to_dict('records'),
        'cells': self.exporter.get_cells(str(xlsx)).to_dict('records')
    }

    # Identify infrastructure-specific elements
    if 'ElementType' in df.columns:
        # Lines and shapes (often roads, boundaries)
        lines = df[df['ElementType'].isin([3, 4, 6, 14])].shape[0]
        analysis['linear_elements'] = lines

        # Complex elements (often structures)
        complex_elements = df[df['ElementType'].isin([12, 14, 18, 19])].shape[0]
        analysis['complex_elements'] = complex_elements

        # Annotation elements
        annotations = df[df['ElementType'].isin([7, 17, 33])].shape[0]
        analysis['annotations'] = annotations

    return analysis

def compare_revisions(self, dgn1: str, dgn2: str) -> Dict[str, Any]:
    """Compare two DGN revisions."""
    xlsx1 = self.exporter.convert(dgn1)
    xlsx2 = self.exporter.convert(dgn2)

    df1 = self.exporter.read_elements(str(xlsx1))
    df2 = self.exporter.read_elements(str(xlsx2))

    levels1 = set(df1['Level'].unique()) if 'Level' in df1.columns else set()
    levels2 = set(df2['Level'].unique()) if 'Level' in df2.columns else set()

    return {
        'revision1': dgn1,
        'revision2': dgn2,
        'element_count_diff': len(df2) - len(df1),
        'levels_added': list(levels2 - levels1),
        'levels_removed': list(levels1 - levels2),
        'common_levels': len(levels1 & levels2)
    }

def extract_coordinates(self, xlsx_file: str) -> pd.DataFrame:
    """Extract element coordinates for GIS integration."""
    df = self.exporter.read_elements(xlsx_file)

    coord_cols = ['ElementId', 'Level', 'ElementType']
    for col in ['RangeLowX', 'RangeLowY', 'RangeLowZ',
                'RangeHighX', 'RangeHighY', 'RangeHighZ',
                'CenterX', 'CenterY', 'CenterZ']:
        if col in df.columns:
            coord_cols.append(col)

    return df[coord_cols].copy()

class DGNLevelManager: """Manage DGN level structures."""

def __init__(self, exporter: DGNExporter):
    self.exporter = exporter

def get_level_map(self, xlsx_file: str) -> Dict[int, str]:
    """Create level number to name mapping."""
    df = self.exporter.read_elements(xlsx_file)

    if 'Level' not in df.columns:
        return {}

    # MicroStation levels are typically numbered 1-63 (V7) or unlimited (V8)
    level_map = {}
    for level in df['Level'].unique():
        level_map[int(level)] = f"Level_{level}"

    return level_map

def filter_by_levels(self, xlsx_file: str,
                     levels: List[int]) -> pd.DataFrame:
    """Filter elements by level numbers."""
    df = self.exporter.read_elements(xlsx_file)
    return df[df['Level'].isin(levels)]

def get_level_usage_report(self, xlsx_file: str) -> pd.DataFrame:
    """Generate level usage report."""
    df = self.exporter.read_elements(xlsx_file)

    if 'Level' not in df.columns or 'ElementType' not in df.columns:
        return pd.DataFrame()

    # Cross-tabulate levels and element types
    report = pd.crosstab(df['Level'], df['ElementType'], margins=True)
    return report

Convenience functions

def convert_dgn_to_excel(dgn_file: str, exporter_path: str = "DgnExporter.exe") -> str: """Quick conversion of DGN to Excel.""" exporter = DGNExporter(exporter_path) output = exporter.convert(dgn_file) return str(output)

def analyze_dgn(dgn_file: str, exporter_path: str = "DgnExporter.exe") -> Dict[str, Any]: """Analyze DGN file and return summary.""" exporter = DGNExporter(exporter_path) analyzer = DGNAnalyzer(exporter) return analyzer.analyze_infrastructure(dgn_file)

Output Structure

Excel Sheets

Sheet Content

Elements All DGN elements with properties

Levels Level definitions

Cells Cell library

Element Columns

Column Type Description

ElementId int Unique element ID

ElementType int Type code (3=Line, 17=Text, etc.)

Level int Level number

Color int Color index

Weight int Line weight

Style int Line style

RangeLowX/Y/Z float Bounding box minimum

RangeHighX/Y/Z float Bounding box maximum

CellName string Cell name (for cell elements)

TextContent string Text content (for text elements)

Quick Start

Initialize exporter

exporter = DGNExporter("C:/DDC/DgnExporter.exe")

Convert DGN to Excel

xlsx = exporter.convert("C:/Projects/Highway.dgn") print(f"Output: {xlsx}")

Read elements

df = exporter.read_elements(str(xlsx)) print(f"Total elements: {len(df)}")

Get level statistics

levels = exporter.get_levels(str(xlsx)) print(levels)

Get element types

types = exporter.get_element_types(str(xlsx)) print(types)

Common Use Cases

1. Infrastructure Analysis

exporter = DGNExporter() analyzer = DGNAnalyzer(exporter)

analysis = analyzer.analyze_infrastructure("highway.dgn") print(f"Total elements: {analysis['statistics']['total_elements']}") print(f"Linear elements: {analysis['linear_elements']}") print(f"Annotations: {analysis['annotations']}")

2. Level Audit

exporter = DGNExporter() xlsx = exporter.convert("bridge.dgn") levels = exporter.get_levels(str(xlsx))

Check for unused standard levels

for idx, row in levels.iterrows(): print(f"Level {row['Level']}: {row['Element_Count']} elements")

3. GIS Integration

analyzer = DGNAnalyzer(exporter) xlsx = exporter.convert("utilities.dgn") coords = analyzer.extract_coordinates(str(xlsx))

Export for GIS

coords.to_csv("coordinates.csv", index=False)

4. Revision Comparison

analyzer = DGNAnalyzer(exporter) diff = analyzer.compare_revisions("rev1.dgn", "rev2.dgn") print(f"Elements changed: {diff['element_count_diff']}")

Integration with DDC Pipeline

Infrastructure pipeline: DGN → Excel → Analysis

from dgn_exporter import DGNExporter, DGNAnalyzer

1. Convert DGN

exporter = DGNExporter("C:/DDC/DgnExporter.exe") xlsx = exporter.convert("highway_project.dgn")

2. Analyze structure

stats = exporter.get_statistics(str(xlsx)) print(f"Elements: {stats['total_elements']}") print(f"Levels: {stats['levels_used']}")

3. Extract for GIS

analyzer = DGNAnalyzer(exporter) coords = analyzer.extract_coordinates(str(xlsx)) coords.to_csv("for_gis.csv", index=False)

Best Practices

• Check version - V7 and V8 have different capabilities

• Reference files - Process all reference files separately

• Level mapping - Document level standards for your organization

• Coordinate systems - Verify units and coordinate systems

• Cell libraries - Export cells separately if needed

Resources

• GitHub: cad2data Pipeline

• DDC Book: Chapter 2.4 - CAD Data Extraction

• MicroStation: Infrastructure-focused CAD software