Name: Bim Classification Ai
Author: majiayu000

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Bim Classification Ai | Skills Pool

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']}%")

Bim Classification Ai

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Bim Classification Ai

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Batch Classification

2. Map to CWICR

3. Quality Check

Resources

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns