ifc-to-excel

name	ifc-to-excel
description	Convert IFC files (2x3, 4x1, 4x3) to Excel databases using IfcExporter CLI. Extract BIM data, properties, and geometry without proprietary software.
homepage	https://datadrivenconstruction.io
metadata	{"openclaw":{"emoji":"📋","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"],"anyBins":["IfcExporter","IfcConvert"]}}}

IFC to Excel Conversion

Business Case

Problem Statement

IFC (Industry Foundation Classes) is the open BIM standard, but:

• Reading IFC requires specialized software
• Property extraction needs programming knowledge
• Batch processing is manual and time-consuming
• Integration with analytics tools is complex

Solution

IfcExporter.exe converts IFC files to structured Excel databases, making BIM data accessible for analysis, validation, and reporting.

Business Value

• Open standard - Process any IFC file (2x3, 4x, 4.3)
• No licenses - Works offline without BIM software
• Data extraction - All properties, quantities, materials
• 3D geometry - Export to Collada DAE format
• Pipeline ready - Integrate with ETL workflows

Technical Implementation

CLI Syntax

IfcExporter.exe <input_ifc> [options]

Supported IFC Versions

Version	Schema	Description
IFC2x3	MVD	Most common exchange format
IFC4	ADD1	Enhanced properties
IFC4x1	Alignment	Infrastructure support
IFC4x3	Latest	Full infrastructure

Output Formats

Output	Description
.xlsx	Excel database with elements and properties
.dae	Collada 3D geometry with matching IDs

Options

Option	Description
bbox	Include element bounding boxes
-no-xlsx	Skip Excel export
-no-collada	Skip 3D geometry export

Examples

# Basic conversion (XLSX + DAE)
IfcExporter.exe "C:\Models\Building.ifc"

# With bounding boxes
IfcExporter.exe "C:\Models\Building.ifc" bbox

# Excel only (no 3D geometry)
IfcExporter.exe "C:\Models\Building.ifc" -no-collada

# Batch processing
for /R "C:\IFC_Models" %f in (*.ifc) do IfcExporter.exe "%f" bbox

Python Integration

import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional, Dict, Any, Set
from dataclasses import dataclass, field
from enum import Enum
import json


class IFCVersion(Enum):
    """IFC schema versions."""
    IFC2X3 = "IFC2X3"
    IFC4 = "IFC4"
    IFC4X1 = "IFC4X1"
    IFC4X3 = "IFC4X3"


class IFCEntityType(Enum):
    """Common IFC entity types."""
    IFCWALL = "IfcWall"
    IFCWALLSTANDARDCASE = "IfcWallStandardCase"
    IFCSLAB = "IfcSlab"
    IFCCOLUMN = "IfcColumn"
    IFCBEAM = "IfcBeam"
    IFCDOOR = "IfcDoor"
    IFCWINDOW = "IfcWindow"
    IFCROOF = "IfcRoof"
    IFCSTAIR = "IfcStair"
    IFCRAILING = "IfcRailing"
    IFCFURNISHINGELEMENT = "IfcFurnishingElement"
    IFCSPACE = "IfcSpace"
    IFCBUILDINGSTOREY = "IfcBuildingStorey"
    IFCBUILDING = "IfcBuilding"
    IFCSITE = "IfcSite"


@dataclass
class IFCElement:
    
    global_id: 
    ifc_type: 
    name: 
    description: []
    object_type: []
    level: []

    
    area: [] = 
    volume: [] = 
    length: [] = 
    height: [] = 
    width: [] = 

    
    bbox_min_x: [] = 
    bbox_min_y: [] = 
    bbox_min_z: [] = 
    bbox_max_x: [] = 
    bbox_max_y: [] = 
    bbox_max_z: [] = 

    
    properties: [, ] = field(default_factory=)
    materials: [] = field(default_factory=)



 :
    
    pset_name: 
    property_name: 
    value: 
    value_type: 



 :
    
    name: 
    category: []
    thickness: []
    layer_position: []


 :
    

     ():
        .exporter = Path(exporter_path)
          .exporter.exists():
             FileNotFoundError()

     () -> Path:
        
        ifc_path = Path(ifc_file)
          ifc_path.exists():
             FileNotFoundError()

        cmd = [(.exporter), (ifc_path)]

         include_bbox:
            cmd.append()
          export_xlsx:
            cmd.append()
          export_collada:
            cmd.append()

        result = subprocess.run(cmd, capture_output=, text=)

         result.returncode != :
             RuntimeError()

         ifc_path.with_suffix()

     () -> [[, ]]:
        
        folder_path = Path(folder)
        pattern =   include_subfolders  

        results = []
         ifc_file  folder_path.glob(pattern):
            :
                output = .convert((ifc_file), include_bbox)
                results.append({
                    : (ifc_file),
                    : (output),
                    : 
                })
                ()
             Exception  e:
                results.append({
                    : (ifc_file),
                    : ,
                    : ,
                    : (e)
                })
                ()

         results

     () -> pd.DataFrame:
        
         pd.read_excel(xlsx_file, sheet_name=)

     () -> pd.DataFrame:
        
        df = .read_elements(xlsx_file)

            df.columns:
             ValueError()

        summary = df.groupby().agg({
            : ,
            :     df.columns  ,
            :     df.columns  
        }).reset_index()

        summary.columns = [, , , ]
         summary.sort_values(, ascending=)

     () -> pd.DataFrame:
        
        df = .read_elements(xlsx_file)

        level_col = 
         col  [, , ]:
             col  df.columns:
                level_col = col
                

         level_col  :
             pd.DataFrame(columns=[, ])

        summary = df.groupby(level_col).agg({
            : 
        }).reset_index()
        summary.columns = [, ]
         summary

     () -> pd.DataFrame:
        
        df = .read_elements(xlsx_file)

            df.columns:
             pd.DataFrame(columns=[, ])

        summary = df.groupby().agg({
            : 
        }).reset_index()
        summary.columns = [, ]
         summary.sort_values(, ascending=)

     () -> pd.DataFrame:
        
        df = .read_elements(xlsx_file)

         group_by   df.columns:
             ValueError()

        agg_dict = {: }

        
        numeric_cols = [, , , , ]
         col  numeric_cols:
             col  df.columns:
                agg_dict[col] = 

        summary = df.groupby(group_by).agg(agg_dict).reset_index()
         summary

     () -> pd.DataFrame:
        
        df = .read_elements(xlsx_file)
         df[df[].isin(ifc_types)]

     () -> [, ]:
        
        df = .read_elements(xlsx_file)
        element = df[df[] == element_id]

         element.empty:
             {}

        
        props = element.iloc[].dropna().to_dict()
         props

     () -> [, ]:
        
        df = .read_elements(xlsx_file)

        validation = {
            : (df),
            : []
        }

        
           df.columns:
            missing_ids = df[].isna().()
             missing_ids > :
                validation[].append()

        
           df.columns:
            missing_names = df[].isna().()
             missing_names > :
                validation[].append()

        
         col  [, ]:
             col  df.columns:
                zero_qty = (df[col] == ).()
                 zero_qty > :
                    validation[].append()

        
           df.columns:
            duplicates = df[].duplicated().()
             duplicates > :
                validation[].append()

        validation[] = (validation[]) == 
         validation


 :
    

     ():
        .exporter = exporter

     () -> [, pd.DataFrame]:
        
        xlsx = .exporter.convert(ifc_file, include_bbox=)
        df = .exporter.read_elements((xlsx))

        qto = {}

        
        walls = df[df[]..contains(, =, na=)]
          walls.empty:
            qto[] = ._summarize_elements(walls, )

        
        slabs = df[df[]..contains(, =, na=)]
          slabs.empty:
            qto[] = ._summarize_elements(slabs, )

        
        columns = df[df[]..contains(, =, na=)]
          columns.empty:
            qto[] = ._summarize_elements(columns, )

        
        beams = df[df[]..contains(, =, na=)]
          beams.empty:
            qto[] = ._summarize_elements(beams, )

        
        doors = df[df[]..contains(, =, na=)]
          doors.empty:
            qto[] = ._summarize_elements(doors, )

        
        windows = df[df[]..contains(, =, na=)]
          windows.empty:
            qto[] = ._summarize_elements(windows, )

         qto

     () -> pd.DataFrame:
        
         group_col   df.columns:
            group_col = 

        agg_dict = {: }
         col  [, , ]:
             col  df.columns:
                agg_dict[col] = 

        summary = df.groupby(group_col).agg(agg_dict).reset_index()
        summary.rename(columns={: }, inplace=)
         summary

     ():
        
         pd.ExcelWriter(output_file, engine=)  writer:
             sheet_name, df  qto.items():
                df.to_excel(writer, sheet_name=sheet_name, index=)



 () -> :
    
    exporter = IFCExporter(exporter_path)
    output = exporter.convert(ifc_file)
     (output)


 () -> [, ]:
    
    df = pd.read_excel(xlsx_file, sheet_name=)

     {
        : (df),
        : df[].nunique()    df.columns  ,
        : df[].nunique()    df.columns  ,
        : df[].()    df.columns  ,
        : df[].()    df.columns  
    }

Output Structure

Excel Sheets

Sheet	Content
Elements	All IFC elements with properties
Types	Element types summary
Levels	Building storey data
Materials	Material assignments
PropertySets	IFC property sets

Element Columns

Column	Type	Description
GlobalId	string	IFC GUID
IfcType	string	IFC entity type
Name	string	Element name
Description	string	Element description
Level	string	Building storey
Material	string	Primary material
Volume	float	Volume (m³)
Area	float	Surface area (m²)
Length	float	Length (m)
Height	float	Height (m)
Width	float	Width (m)

Quick Start

# Initialize exporter
exporter = IFCExporter("C:/DDC/IfcExporter.exe")

# Convert IFC to Excel
xlsx = exporter.convert("C:/Models/Building.ifc", include_bbox=True)

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

# Get element types
types = exporter.get_element_types(str(xlsx))
print(types)

# Get quantities by type
qto = exporter.get_quantities(str(xlsx), group_by='IfcType')
print(qto)

Common Use Cases

1. Model Validation

exporter = IFCExporter()
xlsx = exporter.convert("model.ifc")
validation = exporter.validate_ifc_data(str(xlsx))

if not validation['is_valid']:
    print("Issues found:")
    for issue in validation['issues']:
        print(f"  - {issue}")

2. Quantity Takeoff

qto_generator = IFCQuantityTakeoff(exporter)
qto = qto_generator.generate_qto("building.ifc")

for category, data in qto.items():
    print(f"\n{category}:")
    print(data.to_string(index=False))

3. Material Schedule

xlsx = exporter.convert("building.ifc")
materials = exporter.get_materials(str(xlsx))
print(materials)

Integration with DDC Pipeline

# Full pipeline: IFC → Excel → Validation → Cost Estimate
exporter = IFCExporter("C:/DDC/IfcExporter.exe")

# 1. Convert IFC
xlsx = exporter.convert("project.ifc", include_bbox=True)

# 2. Validate data
validation = exporter.validate_ifc_data(str(xlsx))
print(f"Valid: {validation['is_valid']}")

# 3. Generate QTO
qto = IFCQuantityTakeoff(exporter)
quantities = qto.generate_qto("project.ifc")

# 4. Export for cost estimation
qto.export_to_excel(quantities, "project_qto.xlsx")

Resources

• GitHub: cad2data Pipeline
• IFC Standard: buildingSMART
• DDC Book: Chapter 2.4 - CAD/BIM Data Extraction