Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
BIM models often lack proper classification:
AI-powered classification system that analyzes BIM element properties and suggests appropriate classification codes from multiple standards.
import pandas as pd
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import re
class ClassificationSystem(Enum):
"""Classification standards."""
UNIFORMAT = "uniformat"
MASTERFORMAT = "masterformat"
OMNICLASS = "omniclass"
UNICLASS = "uniclass"
CWICR = "cwicr"
@dataclass
class ClassificationCode:
"""Classification code with metadata."""
code: str
title: str
system: ClassificationSystem
level: int
parent_code: Optional[str] = None
keywords: List[str] = field(default_factory=list)
@dataclass
class ClassificationResult:
"""Result of classification attempt."""
element_id: str
element_name: str
element_category: str
suggested_codes: List[Tuple[ClassificationCode, float]] # (code, confidence)
selected_code: Optional[ClassificationCode] = None
manual_override: bool = False
class ClassificationDatabase:
"""Classification codes database."""
def __init__(self):
self.codes: Dict[ClassificationSystem, List[ClassificationCode]] = {
system: [] for system in ClassificationSystem
}
self._load_standard_codes()
def _load_standard_codes(self):
"""Load standard classification codes."""
# UniFormat II codes
uniformat_codes = [
("A", "Substructure", 1, None, ["foundation", "basement", "excavation"]),
("A10", "Foundations", 2, "A", ["footing", "pile", "foundation"]),
("A1010", "Standard Foundations", 3, "A10", ["spread footing", "strip footing"]),
("A1020", "Special Foundations", 3, "A10", ["pile", "caisson", "mat foundation"]),
("B", "Shell", 1, None, ["superstructure", "exterior", "roof"]),
("B10", "Superstructure", 2, "B", ["floor", "roof", "structure"]),
("B1010", "Floor Construction", 3, "B10", ["slab", "deck", "floor"]),
("B1020", "Roof Construction", 3, "B10", ["roof", "deck", "truss"]),
("B20", "Exterior Enclosure", 2, "B", ["wall", "window", "door"]),
("B2010", "Exterior Walls", 3, "B20", ["curtain wall", "masonry", "cladding"]),
("B2020", "Exterior Windows", 3, "B20", ["window", "glazing", "storefront"]),
("B30", "Roofing", 2, "B", ["roof", "membrane", "insulation"]),
("C", "Interiors", 1, None, ["partition", "ceiling", "floor finish"]),
("C10", "Interior Construction", 2, "C", ["partition", "door", "glazing"]),
("C20", "Stairs", 2, "C", ["stair", "railing", "ladder"]),
("C30", "Interior Finishes", 2, "C", ["finish", "paint", "flooring"]),
("D", "Services", 1, None, ["mechanical", "electrical", "plumbing"]),
("D10", "Conveying", 2, "D", ["elevator", "escalator", "lift"]),
("D20", "Plumbing", 2, "D", ["pipe", "fixture", "drain"]),
("D30", "HVAC", 2, "D", ["duct", "hvac", "air handling"]),
("D40", "Fire Protection", 2, "D", ["sprinkler", "fire", "suppression"]),
("D50", "Electrical", 2, "D", ["electrical", "power", "lighting"]),
]
for code, title, level, parent, keywords in uniformat_codes:
self.codes[ClassificationSystem.UNIFORMAT].append(
ClassificationCode(code, title, ClassificationSystem.UNIFORMAT, level, parent, keywords)
)
# MasterFormat codes (simplified)
masterformat_codes = [
("03", "Concrete", 1, None, ["concrete", "formwork", "reinforcing"]),
("03 30 00", "Cast-in-Place Concrete", 2, "03", ["concrete", "pour", "slab"]),
("03 41 00", "Precast Structural Concrete", 2, "03", ["precast", "concrete", "panel"]),
("04", "Masonry", 1, None, ["brick", "block", "stone"]),
("05", "Metals", 1, None, ["steel", "metal", "aluminum"]),
("05 12 00", "Structural Steel Framing", 2, "05", ["beam", "column", "steel"]),
("06", "Wood, Plastics, Composites", 1, None, ["wood", "timber", "lumber"]),
("07", "Thermal and Moisture Protection", 1, None, ["insulation", "roofing", "waterproofing"]),
("08", "Openings", 1, None, ["door", "window", "glazing"]),
("09", "Finishes", 1, None, ["drywall", "paint", "flooring"]),
("21", "Fire Suppression", 1, None, ["sprinkler", "fire", "suppression"]),
("22", "Plumbing", 1, None, ["pipe", "fixture", "plumbing"]),
("23", "HVAC", 1, None, ["hvac", "duct", "mechanical"]),
("26", "Electrical", 1, None, ["electrical", "power", "lighting"]),
]
for code, title, level, parent, keywords in masterformat_codes:
self.codes[ClassificationSystem.MASTERFORMAT].append(
ClassificationCode(code, title, ClassificationSystem.MASTERFORMAT, level, parent, keywords)
)
def search(self, query: str, system: ClassificationSystem = None) -> List[ClassificationCode]:
"""Search classification codes by keyword."""
results = []
query_lower = query.lower()
systems = [system] if system else list(ClassificationSystem)
for sys in systems:
for code in self.codes.get(sys, []):
# Check title
if query_lower in code.title.lower():
results.append(code)
continue
# Check keywords
if any(query_lower in kw.lower() for kw in code.keywords):
results.append(code)
return results
class BIMClassificationAI:
"""AI-powered BIM element classification."""
def __init__(self, classification_db: ClassificationDatabase = None):
self.db = classification_db or ClassificationDatabase()
self.category_mappings = self._load_category_mappings()
self.results: List[ClassificationResult] = []
def _load_category_mappings(self) -> Dict[str, List[str]]:
"""Load Revit/IFC category to classification mappings."""
return {
# Structural
"Structural Columns": ["B10", "05 12 00", "column", "structural"],
"Structural Framing": ["B10", "05 12 00", "beam", "framing"],
"Structural Foundations": ["A10", "03 30 00", "foundation", "footing"],
"Floors": ["B1010", "03 30 00", "floor", "slab"],
# Architectural
"Walls": ["B20", "04", "wall", "partition"],
"Curtain Walls": ["B2010", "08 44 00", "curtain wall", "glazing"],
"Windows": ["B2020", "08 50 00", "window", "glazing"],
"Doors": ["C10", "08 10 00", "door", "opening"],
"Roofs": ["B30", "07 50 00", "roof", "roofing"],
"Ceilings": ["C30", "09 51 00", "ceiling", "finish"],
"Stairs": ["C20", "05 51 00", "stair", "railing"],
# MEP
"Ducts": ["D30", "23 31 00", "duct", "hvac"],
"Pipes": ["D20", "22 11 00", "pipe", "plumbing"],
"Electrical Equipment": ["D50", "26 20 00", "electrical", "panel"],
"Lighting Fixtures": ["D50", "26 51 00", "light", "fixture"],
"Sprinklers": ["D40", "21 13 00", "sprinkler", "fire protection"],
"Mechanical Equipment": ["D30", "23 70 00", "ahu", "hvac equipment"],
}
def classify_element(self,
element_id: str,
element_name: str,
category: str,
properties: Dict[str, Any] = None,
target_systems: List[ClassificationSystem] = None) -> ClassificationResult:
"""Classify a single BIM element."""
target_systems = target_systems or [ClassificationSystem.UNIFORMAT, ClassificationSystem.MASTERFORMAT]
suggestions = []
# Get keywords from category mapping
keywords = self.category_mappings.get(category, [])
# Add keywords from element name
name_words = re.findall(r'\w+', element_name.lower())
keywords.extend(name_words)
# Add keywords from properties
if properties:
for key, value in properties.items():
if isinstance(value, str):
keywords.extend(re.findall(r'\w+', value.lower()))
# Search classification codes
for system in target_systems:
for keyword in keywords:
matches = self.db.search(keyword, system)
for match in matches:
confidence = self._calculate_confidence(match, keywords, category)
suggestions.append((match, confidence))
# Remove duplicates and sort by confidence
seen = set()
unique_suggestions = []
for code, conf in sorted(suggestions, key=lambda x: x[1], reverse=True):
if code.code not in seen:
seen.add(code.code)
unique_suggestions.append((code, conf))
result = ClassificationResult(
element_id=element_id,
element_name=element_name,
element_category=category,
suggested_codes=unique_suggestions[:5],
selected_code=unique_suggestions[0][0] if unique_suggestions else None
)
self.results.append(result)
return result
def _calculate_confidence(self, code: ClassificationCode,
keywords: List[str], category: str) -> float:
"""Calculate classification confidence score."""
score = 0.0
# Direct category match
if category in self.category_mappings:
if code.code in self.category_mappings[category]:
score += 0.5
# Keyword matches
keyword_matches = sum(1 for kw in keywords if kw.lower() in
[k.lower() for k in code.keywords])
score += min(keyword_matches * 0.1, 0.3)
# Title match
title_words = code.title.lower().split()
title_matches = sum(1 for kw in keywords if kw.lower() in title_words)
score += min(title_matches * 0.1, 0.2)
return min(score, 1.0)
def classify_batch(self, elements_df: pd.DataFrame,
id_column: str = 'element_id',
name_column: str = 'name',
category_column: str = 'category') -> pd.DataFrame:
"""Classify multiple elements from DataFrame."""
results = []
for _, row in elements_df.iterrows():
result = self.classify_element(
element_id=str(row[id_column]),
element_name=str(row[name_column]),
category=str(row[category_column]),
properties=row.to_dict()
)
results.append({
'element_id': result.element_id,
'element_name': result.element_name,
'category': result.element_category,
'uniformat_code': next((c.code for c, _ in result.suggested_codes
if c.system == ClassificationSystem.UNIFORMAT), None),
'masterformat_code': next((c.code for c, _ in result.suggested_codes
if c.system == ClassificationSystem.MASTERFORMAT), None),
'confidence': result.suggested_codes[0][1] if result.suggested_codes else 0
})
return pd.DataFrame(results)
def get_summary(self) -> Dict[str, Any]:
"""Get classification summary."""
total = len(self.results)
classified = sum(1 for r in self.results if r.selected_code)
high_confidence = sum(1 for r in self.results
if r.suggested_codes and r.suggested_codes[0][1] > 0.7)
return {
'total_elements': total,
'classified': classified,
'classification_rate': round(classified / total * 100, 1) if total > 0 else 0,
'high_confidence': high_confidence,
'high_confidence_rate': round(high_confidence / total * 100, 1) if total > 0 else 0
}
def export_results(self) -> pd.DataFrame:
"""Export classification results to DataFrame."""
data = []
for result in self.results:
row = {
'element_id': result.element_id,
'element_name': result.element_name,
'category': result.element_category,
'selected_code': result.selected_code.code if result.selected_code else None,
'selected_title': result.selected_code.title if result.selected_code else None,
'selected_system': result.selected_code.system.value if result.selected_code else None,
'manual_override': result.manual_override
}
# Add top suggestions
for i, (code, conf) in enumerate(result.suggested_codes[:3]):
row[f'suggestion_{i+1}_code'] = code.code
row[f'suggestion_{i+1}_confidence'] = round(conf, 2)
data.append(row)
return pd.DataFrame(data)
# Initialize classifier
classifier = BIMClassificationAI()
# Classify single element
result = classifier.classify_element(
element_id="12345",
element_name="Concrete Floor Slab Level 2",
category="Floors",
properties={'material': 'Concrete', 'thickness': '200mm'}
)
print(f"Suggested: {result.selected_code.code} - {result.selected_code.title}")
print(f"Confidence: {result.suggested_codes[0][1]:.1%}")
# Load BIM elements
elements = pd.read_excel("bim_elements.xlsx")
# Classify all
classified = classifier.classify_batch(elements)
classified.to_excel("classified_elements.xlsx")
# Get UniFormat code for cost mapping
uniformat = result.selected_code.code
cwicr_code = map_uniformat_to_cwicr(uniformat)
summary = classifier.get_summary()
print(f"Classification rate: {summary['classification_rate']}%")
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
日本語の概要は準備中です。原文の説明を表示しています。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
日本語の概要は準備中です。原文の説明を表示しています。