IFC Data Extraction

Overview

This skill provides comprehensive IFC file parsing and data extraction using IfcOpenShell. Extract element data, quantities, properties, and relationships from BIM models for analysis and reporting.

Based on Open BIM Standards - Working with vendor-neutral IFC format for maximum interoperability.

"IFC является открытым стандартом для обмена BIM-данными, позволяющим извлекать информацию независимо от программного обеспечения." — DDC Methodology

Quick Start

import ifcopenshell import ifcopenshell.util.element as element_util import pandas as pd

Open IFC file

ifc = ifcopenshell.open("model.ifc")

Get project info

project = ifc.by_type("IfcProject")[0] print(f"Project: {project.Name}")

Extract all walls

walls = ifc.by_type("IfcWall") print(f"Total walls: {len(walls)}")

Get wall data

wall_data = [] for wall in walls: psets = element_util.get_psets(wall) wall_data.append({ 'GlobalId': wall.GlobalId, 'Name': wall.Name, 'Type': wall.is_a(), 'Level': get_level(wall), 'Properties': psets })

df = pd.DataFrame(wall_data) print(df.head())

Core Extraction Functions

Element Extractor Class

import ifcopenshell import ifcopenshell.util.element as element_util import ifcopenshell.util.placement as placement_util import ifcopenshell.geom import pandas as pd from typing import List, Dict, Optional, Any

class IFCExtractor: """Extract data from IFC files"""

def __init__(self, ifc_path: str):
    self.model = ifcopenshell.open(ifc_path)
    self.settings = ifcopenshell.geom.settings()

def get_project_info(self) -> Dict:
    """Extract project metadata"""
    project = self.model.by_type("IfcProject")[0]
    site = self.model.by_type("IfcSite")
    building = self.model.by_type("IfcBuilding")

    return {
        'project_id': project.GlobalId,
        'project_name': project.Name,
        'description': project.Description,
        'site_count': len(site),
        'building_count': len(building),
        'schema': self.model.schema
    }

def get_all_elements(self, element_types: List[str] = None) -> pd.DataFrame:
    """Extract all elements of specified types"""
    if element_types is None:
        element_types = [
            'IfcWall', 'IfcSlab', 'IfcColumn', 'IfcBeam',
            'IfcDoor', 'IfcWindow', 'IfcStair', 'IfcRoof'
        ]

    all_elements = []

    for ifc_type in element_types:
        elements = self.model.by_type(ifc_type)

        for elem in elements:
            data = self._extract_element_data(elem)
            data['IFC_Type'] = ifc_type
            all_elements.append(data)

    return pd.DataFrame(all_elements)

def _extract_element_data(self, element) -> Dict:
    """Extract data from single element"""
    # Basic info
    data = {
        'GlobalId': element.GlobalId,
        'Name': element.Name,
        'Description': element.Description,
        'ObjectType': element.ObjectType if hasattr(element, 'ObjectType') else None
    }

    # Get level/storey
    data['Level'] = self._get_element_level(element)

    # Get material
    data['Material'] = self._get_element_material(element)

    # Get type
    data['TypeName'] = self._get_element_type(element)

    # Get all property sets
    psets = element_util.get_psets(element)
    data['PropertySets'] = psets

    # Extract common quantities
    base_quantities = psets.get('BaseQuantities', {})
    data.update({
        'Length': base_quantities.get('Length'),
        'Width': base_quantities.get('Width'),
        'Height': base_quantities.get('Height'),
        'Area': base_quantities.get('NetSideArea') or base_quantities.get('GrossArea'),
        'Volume': base_quantities.get('NetVolume') or base_quantities.get('GrossVolume')
    })

    return data

def _get_element_level(self, element) -> Optional[str]:
    """Get the building storey for an element"""
    if hasattr(element, 'ContainedInStructure'):
        for rel in element.ContainedInStructure or []:
            if rel.RelatingStructure.is_a('IfcBuildingStorey'):
                return rel.RelatingStructure.Name
    return None

def _get_element_material(self, element) -> Optional[str]:
    """Get material name for element"""
    if hasattr(element, 'HasAssociations'):
        for rel in element.HasAssociations or []:
            if rel.is_a('IfcRelAssociatesMaterial'):
                material = rel.RelatingMaterial
                if hasattr(material, 'Name'):
                    return material.Name
                elif hasattr(material, 'ForLayerSet'):
                    layers = material.ForLayerSet.MaterialLayers
                    if layers:
                        return layers[0].Material.Name
    return None

def _get_element_type(self, element) -> Optional[str]:
    """Get element type name"""
    if hasattr(element, 'IsTypedBy'):
        for rel in element.IsTypedBy or []:
            return rel.RelatingType.Name
    return None

def extract_quantities(self) -> pd.DataFrame:
    """Extract quantities for all elements"""
    elements = self.get_all_elements()

    # Group by category and level
    quantities = elements.groupby(['IFC_Type', 'Level']).agg({
        'GlobalId': 'count',
        'Volume': 'sum',
        'Area': 'sum',
        'Length': 'sum'
    }).rename(columns={'GlobalId': 'Count'}).reset_index()

    return quantities

def extract_levels(self) -> pd.DataFrame:
    """Extract building levels/storeys"""
    storeys = self.model.by_type("IfcBuildingStorey")

    level_data = []
    for storey in storeys:
        level_data.append({
            'GlobalId': storey.GlobalId,
            'Name': storey.Name,
            'Elevation': storey.Elevation,
            'Description': storey.Description
        })

    return pd.DataFrame(level_data).sort_values('Elevation')

def extract_spaces(self) -> pd.DataFrame:
    """Extract spaces/rooms"""
    spaces = self.model.by_type("IfcSpace")

    space_data = []
    for space in spaces:
        psets = element_util.get_psets(space)
        base_qty = psets.get('BaseQuantities', {})

        space_data.append({
            'GlobalId': space.GlobalId,
            'Name': space.Name,
            'LongName': space.LongName,
            'Level': self._get_element_level(space),
            'Area': base_qty.get('NetFloorArea'),
            'Volume': base_qty.get('NetVolume'),
            'Height': base_qty.get('Height')
        })

    return pd.DataFrame(space_data)

def extract_materials(self) -> pd.DataFrame:
    """Extract material summary"""
    materials = {}

    for elem in self.model.by_type("IfcProduct"):
        material = self._get_element_material(elem)
        if material:
            if material not in materials:
                materials[material] = {'count': 0, 'volume': 0}

            materials[material]['count'] += 1

            psets = element_util.get_psets(elem)
            volume = psets.get('BaseQuantities', {}).get('NetVolume', 0)
            if volume:
                materials[material]['volume'] += volume

    return pd.DataFrame.from_dict(materials, orient='index').reset_index()

def extract_relationships(self) -> pd.DataFrame:
    """Extract element relationships"""
    relationships = []

    # Spatial containment
    for rel in self.model.by_type("IfcRelContainedInSpatialStructure"):
        for elem in rel.RelatedElements:
            relationships.append({
                'Element': elem.GlobalId,
                'Element_Type': elem.is_a(),
                'Relationship': 'ContainedIn',
                'Related_To': rel.RelatingStructure.GlobalId,
                'Related_Type': rel.RelatingStructure.is_a()
            })

    # Aggregation
    for rel in self.model.by_type("IfcRelAggregates"):
        for part in rel.RelatedObjects:
            relationships.append({
                'Element': part.GlobalId,
                'Element_Type': part.is_a(),
                'Relationship': 'PartOf',
                'Related_To': rel.RelatingObject.GlobalId,
                'Related_Type': rel.RelatingObject.is_a()
            })

    return pd.DataFrame(relationships)

Geometry Extraction

Extract Geometry Data

import numpy as np

class IFCGeometryExtractor: """Extract geometry data from IFC elements"""

def __init__(self, ifc_path: str):
    self.model = ifcopenshell.open(ifc_path)
    self.settings = ifcopenshell.geom.settings()
    self.settings.set(self.settings.USE_WORLD_COORDS, True)

def get_element_geometry(self, element) -> Dict:
    """Extract geometry for single element"""
    try:
        shape = ifcopenshell.geom.create_shape(self.settings, element)

        verts = shape.geometry.verts
        faces = shape.geometry.faces

        # Calculate bounding box
        vertices = np.array(verts).reshape(-1, 3)
        min_coords = vertices.min(axis=0)
        max_coords = vertices.max(axis=0)
        dimensions = max_coords - min_coords

        return {
            'GlobalId': element.GlobalId,
            'vertices_count': len(vertices),
            'faces_count': len(faces) // 3,
            'min_x': min_coords[0],
            'min_y': min_coords[1],
            'min_z': min_coords[2],
            'max_x': max_coords[0],
            'max_y': max_coords[1],
            'max_z': max_coords[2],
            'length': dimensions[0],
            'width': dimensions[1],
            'height': dimensions[2],
            'center_x': (min_coords[0] + max_coords[0]) / 2,
            'center_y': (min_coords[1] + max_coords[1]) / 2,
            'center_z': (min_coords[2] + max_coords[2]) / 2
        }
    except:
        return {'GlobalId': element.GlobalId, 'error': 'Geometry extraction failed'}

def get_bounding_boxes(self, element_type: str) -> pd.DataFrame:
    """Get bounding boxes for all elements of type"""
    elements = self.model.by_type(element_type)
    boxes = [self.get_element_geometry(e) for e in elements]
    return pd.DataFrame(boxes)

def calculate_volumes(self, element_type: str) -> pd.DataFrame:
    """Calculate volumes using geometry"""
    elements = self.model.by_type(element_type)
    volumes = []

    for elem in elements:
        try:
            shape = ifcopenshell.geom.create_shape(self.settings, elem)
            # Calculate volume from mesh (simplified)
            verts = np.array(shape.geometry.verts).reshape(-1, 3)
            bbox_volume = np.prod(verts.max(axis=0) - verts.min(axis=0))

            volumes.append({
                'GlobalId': elem.GlobalId,
                'Name': elem.Name,
                'BBox_Volume': bbox_volume
            })
        except:
            pass

    return pd.DataFrame(volumes)

Export Functions

Export to Various Formats

class IFCExporter: """Export IFC data to various formats"""

def __init__(self, extractor: IFCExtractor):
    self.extractor = extractor

def to_excel(self, output_path: str, include_all: bool = True):
    """Export to Excel with multiple sheets"""
    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
        # Project info
        project_info = pd.DataFrame([self.extractor.get_project_info()])
        project_info.to_excel(writer, sheet_name='Project', index=False)

        # All elements
        if include_all:
            elements = self.extractor.get_all_elements()
            elements.to_excel(writer, sheet_name='Elements', index=False)

        # Quantities
        quantities = self.extractor.extract_quantities()
        quantities.to_excel(writer, sheet_name='Quantities', index=False)

        # Levels
        levels = self.extractor.extract_levels()
        levels.to_excel(writer, sheet_name='Levels', index=False)

        # Spaces
        spaces = self.extractor.extract_spaces()
        spaces.to_excel(writer, sheet_name='Spaces', index=False)

        # Materials
        materials = self.extractor.extract_materials()
        materials.to_excel(writer, sheet_name='Materials', index=False)

    return output_path

def to_csv(self, output_dir: str):
    """Export to multiple CSV files"""
    import os
    os.makedirs(output_dir, exist_ok=True)

    exports = {
        'elements.csv': self.extractor.get_all_elements(),
        'quantities.csv': self.extractor.extract_quantities(),
        'levels.csv': self.extractor.extract_levels(),
        'spaces.csv': self.extractor.extract_spaces(),
        'materials.csv': self.extractor.extract_materials()
    }

    for filename, df in exports.items():
        df.to_csv(os.path.join(output_dir, filename), index=False)

    return output_dir

def to_json(self, output_path: str):
    """Export to JSON"""
    import json

    data = {
        'project': self.extractor.get_project_info(),
        'elements': self.extractor.get_all_elements().to_dict('records'),
        'quantities': self.extractor.extract_quantities().to_dict('records'),
        'levels': self.extractor.extract_levels().to_dict('records'),
        'materials': self.extractor.extract_materials().to_dict('records')
    }

    with open(output_path, 'w', encoding='utf-8') as f:
        json.dump(data, f, indent=2, default=str)

    return output_path

def to_database(self, connection_string: str, table_prefix: str = 'ifc_'):
    """Export to SQL database"""
    from sqlalchemy import create_engine

    engine = create_engine(connection_string)

    tables = {
        f'{table_prefix}elements': self.extractor.get_all_elements(),
        f'{table_prefix}quantities': self.extractor.extract_quantities(),
        f'{table_prefix}levels': self.extractor.extract_levels(),
        f'{table_prefix}spaces': self.extractor.extract_spaces(),
        f'{table_prefix}materials': self.extractor.extract_materials()
    }

    for table_name, df in tables.items():
        # Remove complex columns for database storage
        simple_df = df.select_dtypes(exclude=['object']).copy()
        for col in df.columns:
            if df[col].dtype == 'object':
                simple_df[col] = df[col].astype(str)

        simple_df.to_sql(table_name, engine, if_exists='replace', index=False)

    return list(tables.keys())

Quick Reference

Element Type Common Properties Quantities

IfcWall IsExternal, FireRating Length, Height, Area, Volume

IfcSlab IsExternal, LoadBearing Area, Volume, Perimeter

IfcColumn LoadBearing Height, CrossSectionArea

IfcBeam LoadBearing Length, CrossSectionArea

IfcDoor FireRating, AcousticRating Width, Height

IfcWindow ThermalTransmittance Width, Height, Area

Property Set Lookup

Common IFC Property Sets

PSETS = { 'Pset_WallCommon': ['IsExternal', 'LoadBearing', 'FireRating'], 'Pset_SlabCommon': ['IsExternal', 'LoadBearing', 'AcousticRating'], 'Pset_ColumnCommon': ['IsExternal', 'LoadBearing'], 'Pset_BeamCommon': ['LoadBearing', 'FireRating'], 'Pset_DoorCommon': ['FireRating', 'AcousticRating', 'SecurityRating'], 'Pset_WindowCommon': ['ThermalTransmittance', 'GlazingType'], 'BaseQuantities': ['Length', 'Width', 'Height', 'Area', 'Volume'] }

Resources

• IfcOpenShell: https://ifcopenshell.org

• IFC Standard: https://www.buildingsmart.org/standards/bsi-standards/industry-foundation-classes/

• DDC Website: https://datadrivenconstruction.io

Next Steps

• See bim-validation-pipeline for validating extracted data

• See qto-report for quantity take-off reports

• See 4d-simulation for linking to schedules