Detalle del Skill

bim-clash-detection

Detects architectural, structural, and MEP clashes before construction.

CoincidenciaDirectaRevisado para construcción
Fuentedatadrivenconstruction/ddc_skills_for_ai_agents_in_constructionFuente externa
Instalaciones reportadas104Solo señal de popularidad

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SKILL.md

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---
name: "bim-clash-detection"
description: "Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw": {"emoji": "🔍", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
---
# BIM Clash Detection

## Business Case

### Problem Statement
Coordination issues cause significant rework:
- MEP vs structural conflicts discovered on site
- Late design changes increase costs
- Manual clash review is time-consuming
- No standardized clash categorization

### Solution
Automated clash detection and analysis system that identifies conflicts between building systems and provides prioritized resolution recommendations.

### Business Value
- **Cost savings** - Detect issues before construction
- **Time reduction** - Automated clash identification
- **Better coordination** - Systematic conflict resolution
- **Quality improvement** - Fewer field issues

## Technical Implementation

```python
import pandas as pd
from datetime import datetime
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import math


class ClashType(Enum):
    """Types of clashes."""
    HARD = "hard"           # Physical intersection
    SOFT = "soft"           # Clearance violation
    WORKFLOW = "workflow"   # Sequencing conflict
    DUPLICATE = "duplicate" # Duplicated elements


class ClashStatus(Enum):
    """Clash resolution status."""
    NEW = "new"
    ACTIVE = "active"
    RESOLVED = "resolved"
    APPROVED = "approved"
    IGNORED = "ignored"


class ClashSeverity(Enum):
    """Clash severity level."""
    CRITICAL = "critical"
    MAJOR = "major"
    MINOR = "minor"
    INFO = "info"


class Discipline(Enum):
    """BIM disciplines."""
    ARCHITECTURAL = "architectural"
    STRUCTURAL = "structural"
    MECHANICAL = "mechanical"
    ELECTRICAL = "electrical"
    PLUMBING = "plumbing"
    FIRE_PROTECTION = "fire_protection"
    CIVIL = "civil"


@dataclass
class BoundingBox:
    """3D bounding box."""
    min_x: float
    min_y: float
    min_z: float
    max_x: float
    max_y: float
    max_z: float

    def intersects(self, other: 'BoundingBox') -> bool:
        """Check if boxes intersect."""
        return (self.min_x <= other.max_x and self.max_x >= other.min_x and
                self.min_y <= other.max_y and self.max_y >= other.min_y and
                self.min_z <= other.max_z and self.max_z >= other.min_z)

    def volume(self) -> float:
        """Calculate bounding box volume."""
        return ((self.max_x - self.min_x) *
                (self.max_y - self.min_y) *
                (self.max_z - self.min_z))

    def center(self) -> Tuple[float, float, float]:
        """Get center point."""
        return (
            (self.min_x + self.max_x) / 2,
            (self.min_y + self.max_y) / 2,
            (self.min_z + self.max_z) / 2
        )


@dataclass
class BIMElement:
    """BIM element representation."""
    element_id: str
    name: str
    discipline: Discipline
    category: str  # e.g., "Duct", "Beam", "Pipe"
    level: str
    bounding_box: BoundingBox
    properties: Dict[str, Any] = field(default_factory=dict)

    def distance_to(self, other: 'BIMElement') -> float:
        """Calculate distance between element centers."""
        c1 = self.bounding_box.center()
        c2 = other.bounding_box.center()
        return math.sqrt(
            (c2[0] - c1[0])**2 +
            (c2[1] - c1[1])**2 +
            (c2[2] - c1[2])**2
        )


@dataclass
class Clash:
    """Clash between two elements."""
    clash_id: str
    element_a: BIMElement
    element_b: BIMElement
    clash_type: ClashType
    severity: ClashSeverity
    status: ClashStatus
    distance: float  # Penetration depth (negative) or clearance gap
    location: Tuple[float, float, float]
    detected_at:
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