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Bim Clash Detection

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datadrivenconstruction
bim-clash-detection

Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.

Overview

Publisherdatadrivenconstruction
RepositoryDDC_Skills_for_AI_Agents_in_Construction
Skill namebim-clash-detection
Stars
365
Forks
91
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by datadrivenconstruction on GitHub. Read the source before you install it.

Installation

Install the Bim Clash Detection AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/BIM-Analysis/bim-clash-detection
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git /tmp/DDC_Skills_for_AI_Agents_in_Construction
mkdir -p .claude/skills
cp -r /tmp/DDC_Skills_for_AI_Agents_in_Construction/1_DDC_Toolkit/BIM-Analysis/bim-clash-detection .claude/skills/bim-clash-detection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bim Clash Detection in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bim Clash Detection on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bim Clash Detection is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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: datetime
    resolved_at: Optional[datetime] = None
    assigned_to: Optional[str] = None
    notes: str = ""

    def to_dict(self) -> Dict[str, Any]:
        return {
            'clash_id': self.clash_id,
            'element_a_id': self.element_a.element_id,
            'element_a_name': self.element_a.name,
            'element_a_discipline': self.element_a.discipline.value,
            'element_b_id': self.element_b.element_id,
            'element_b_name': self.element_b.name,
            'element_b_discipline': self.element_b.discipline.value,
            'clash_type': self.clash_type.value,
            'severity': self.severity.value,
            'status': self.status.value,
            'distance': round(self.distance, 3),
            'location_x': self.location[0],
            'location_y': self.location[1],
            'location_z': self.location[2],
            'level': self.element_a.level,
            'detected_at': self.detected_at.isoformat(),
            'assigned_to': self.assigned_to,
            'notes': self.notes
        }


@dataclass
class ClashTest:
    """Clash test configuration."""
    name: str
    discipline_a: Discipline
    discipline_b: Discipline
    clash_type: ClashType
    tolerance: float = 0.0  # Clearance tolerance in meters
    enabled: bool = True


class BIMClashDetector:
    """Detect and manage BIM clashes."""

    def __init__(self):
        self.elements: List[BIMElement] = []
        self.clashes: List[Clash] = []
        self.clash_tests: List[ClashTest] = []
        self._clash_counter = 0

    def load_elements(self, elements_df: pd.DataFrame) -> int:
        """Load BIM elements from DataFrame."""
        loaded = 0
        for _, row in elements_df.iterrows():
            element = BIMElement(
                element_id=str(row.get('element_id', '')),
                name=str(row.get('name', '')),
                discipline=Discipline(row.get('discipline', 'architectural')),
                category=str(row.get('category', '')),
                level=str(row.get('level', '')),
                bounding_box=BoundingBox(
                    min_x=float(row.get('min_x', 0)),
                    min_y=float(row.get('min_y', 0)),
                    min_z=float(row.get('min_z', 0)),
                    max_x=float(row.get('max_x', 0)),
                    max_y=float(row.get('max_y', 0)),
                    max_z=float(row.get('max_z', 0))
                )
            )
            self.elements.append(element)
            loaded += 1
        return loaded

    def add_clash_test(self, test: ClashTest):
        """Add clash test configuration."""
        self.clash_tests.append(test)

    def setup_standard_tests(self):
        """Setup standard MEP coordination tests."""
        standard_tests = [
            ClashTest("MEP vs Structure", Discipline.MECHANICAL, Discipline.STRUCTURAL, ClashType.HARD),
            ClashTest("Electrical vs Structure", Discipline.ELECTRICAL, Discipline.STRUCTURAL, ClashType.HARD),
            ClashTest("Plumbing vs Structure", Discipline.PLUMBING, Discipline.STRUCTURAL, ClashType.HARD),
            ClashTest("MEP vs MEP", Discipline.MECHANICAL, Discipline.ELECTRICAL, ClashType.HARD),
            ClashTest("Duct Clearance", Discipline.MECHANICAL, Discipline.MECHANICAL, ClashType.SOFT, tolerance=0.05),
            ClashTest("Fire Protection", Discipline.FIRE_PROTECTION, Discipline.STRUCTURAL, ClashType.HARD),
        ]
        for test in standard_tests:
            self.add_clash_test(test)

    def run_clash_detection(self) -> List[Clash]:
        """Run all clash tests."""
        new_clashes = []

        for test in self.clash_tests:
            if not test.enabled:
                continue

            # Filter elements by discipline
            elements_a = [e for e in self.elements if e.discipline == test.discipline_a]
            elements_b = [e for e in self.elements if e.discipline == test.discipline_b]

            # Check all pairs
            for elem_a in elements_a:
                for elem_b in elements_b:
                    if elem_a.element_id == elem_b.element_id:
                        continue

                    clash = self._check_clash(elem_a, elem_b, test)
                    if clash:
                        new_clashes.append(clash)

        self.clashes.extend(new_clashes)
        return new_clashes

    def _check_clash(self, elem_a: BIMElement, elem_b: BIMElement,
                     test: ClashTest) -> Optional[Clash]:
        """Check if two elements clash."""

        # Expand bounding box by tolerance for soft clashes
        box_a = elem_a.bounding_box
        box_b = elem_b.bounding_box

        if test.clash_type == ClashType.SOFT:
            # Add clearance tolerance
            expanded_a = BoundingBox(
                box_a.min_x - test.tolerance, box_a.min_y - test.tolerance, box_a.min_z - test.tolerance,
                box_a.max_x + test.tolerance, box_a.max_y + test.tolerance, box_a.max_z + test.tolerance
            )
            intersects = expanded_a.intersects(box_b)
        else:
            intersects = box_a.intersects(box_b)

        if not intersects:
            return None

        # Calculate clash point and severity
        self._clash_counter += 1
        clash_id = f"CLH-{self._clash_counter:05d}"

        # Clash location (center of intersection)
        location = (
            (max(box_a.min_x, box_b.min_x) + min(box_a.max_x, box_b.max_x)) / 2,
            (max(box_a.min_y, box_b.min_y) + min(box_a.max_y, box_b.max_y)) / 2,
            (max(box_a.min_z, box_b.min_z) + min(box_a.max_z, box_b.max_z)) / 2
        )

        # Calculate penetration depth
        distance = elem_a.distance_to(elem_b)

        # Determine severity
        if test.clash_type == ClashType.HARD:
            severity = ClashSeverity.CRITICAL if distance < 0.1 else ClashSeverity.MAJOR
        else:
            severity = ClashSeverity.MINOR if distance > test.tolerance else ClashSeverity.MAJOR

        return Clash(
            clash_id=clash_id,
            element_a=elem_a,
            element_b=elem_b,
            clash_type=test.clash_type,
            severity=severity,
            status=ClashStatus.NEW,
            distance=distance,
            location=location,
            detected_at=datetime.now()
        )

    def get_summary(self) -> Dict[str, Any]:
        """Get clash detection summary."""
        by_severity = {}
        by_discipline = {}
        by_status = {}

        for clash in self.clashes:
            # By severity
            sev = clash.severity.value
            by_severity[sev] = by_severity.get(sev, 0) + 1

            # By discipline pair
            pair = f"{clash.element_a.discipline.value} vs {clash.element_b.discipline.value}"
            by_discipline[pair] = by_discipline.get(pair, 0) + 1

            # By status
            stat = clash.status.value
            by_status[stat] = by_status.get(stat, 0) + 1

        return {
            'total_clashes': len(self.clashes),
            'by_severity': by_severity,
            'by_discipline': by_discipline,
            'by_status': by_status,
            'elements_checked': len(self.elements),
            'tests_run': len([t for t in self.clash_tests if t.enabled])
        }

    def export_to_dataframe(self) -> pd.DataFrame:
        """Export clashes to DataFrame."""
        return pd.DataFrame([c.to_dict() for c in self.clashes])

    def resolve_clash(self, clash_id: str, resolution_note: str):
        """Mark clash as resolved."""
        for clash in self.clashes:
            if clash.clash_id == clash_id:
                clash.status = ClashStatus.RESOLVED
                clash.resolved_at = datetime.now()
                clash.notes = resolution_note
                break

    def assign_clash(self, clash_id: str, assignee: str):
        """Assign clash to team member."""
        for clash in self.clashes:
            if clash.clash_id == clash_id:
                clash.assigned_to = assignee
                clash.status = ClashStatus.ACTIVE
                break

Quick Start

python
# Initialize detector
detector = BIMClashDetector()

# Setup standard MEP tests
detector.setup_standard_tests()

# Load elements from DataFrame
elements_df = pd.read_excel("bim_elements.xlsx")
detector.load_elements(elements_df)

# Run detection
clashes = detector.run_clash_detection()
print(f"Found {len(clashes)} clashes")

# Get summary
summary = detector.get_summary()
print(f"Critical: {summary['by_severity'].get('critical', 0)}")

Common Use Cases

1. MEP Coordination

python
# Focus on MEP vs Structure
mep_clashes = [c for c in detector.clashes
               if c.element_a.discipline in [Discipline.MECHANICAL, Discipline.ELECTRICAL]]

2. Export for Review

python
df = detector.export_to_dataframe()
df.to_excel("clash_report.xlsx", index=False)

3. Assign to Teams

python
for clash in detector.clashes:
    if clash.element_a.discipline == Discipline.MECHANICAL:
        detector.assign_clash(clash.clash_id, "MEP Team")

Resources

  • DDC Book: Chapter 2.4 - BIM Coordination
  • Reference: ISO 19650 BIM Standards

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bim Clash Detection AI skill do?

Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.

Why use Bim Clash Detection on TypingMind?

Because you install it once and use it with any model. Bim Clash Detection is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bim Clash Detection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/BIM-Analysis/bim-clash-detection. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bim Clash Detection?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bim Clash Detection?

As many as you like. As long as a model supports skills, you can use Bim Clash Detection with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bim Clash Detection AI skill free?

Yes. It is published on GitHub by datadrivenconstruction under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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