bim-validation-report
This Claude Code skill automatically validates BIM models against configurable quality rules and generates detailed compliance reports. Use it when you need to verify BIM model data quality, check for missing required properties, ensure naming conventions and classification standards are met, and identify non-compliant or incomplete model information before project delivery or coordination reviews.
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction /tmp/bim-validation-report && cp -r /tmp/bim-validation-report/1_DDC_Toolkit/BIM-Analysis/bim-validation-report ~/.claude/skills/bim-validation-reportSKILL.md
# BIM Validation Report Generator
## Business Case
### Problem Statement
BIM models often have quality issues:
- Missing required properties
- Invalid or inconsistent data
- Non-compliant with project standards
- Incomplete model information
### Solution
Automated BIM validation system that checks models against configurable rules and generates detailed compliance reports.
### Business Value
- **Quality assurance** - Catch issues early
- **Standards compliance** - Meet project requirements
- **Automation** - Reduce manual QC effort
- **Transparency** - Clear validation results
## Technical Implementation
```python
import pandas as pd
from datetime import datetime
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
class ValidationSeverity(Enum):
"""Validation issue severity."""
ERROR = "error"
WARNING = "warning"
INFO = "info"
class ValidationStatus(Enum):
"""Overall validation status."""
PASSED = "passed"
PASSED_WITH_WARNINGS = "passed_with_warnings"
FAILED = "failed"
class RuleCategory(Enum):
"""Validation rule categories."""
REQUIRED_PROPERTIES = "required_properties"
DATA_FORMAT = "data_format"
NAMING_CONVENTION = "naming_convention"
GEOMETRIC = "geometric"
CLASSIFICATION = "classification"
RELATIONSHIPS = "relationships"
@dataclass
class ValidationRule:
"""Single validation rule."""
rule_id: str
name: str
category: RuleCategory
description: str
severity: ValidationSeverity
check_function: Callable
applicable_categories: List[str] = field(default_factory=list)
enabled: bool = True
@dataclass
class ValidationIssue:
"""Single validation issue."""
issue_id: str
rule_id: str
rule_name: str
element_id: str
element_name: str
element_category: str
severity: ValidationSeverity
message: str
details: Dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> Dict[str, Any]:
return {
'issue_id': self.issue_id,
'rule_id': self.rule_id,
'rule_name': self.rule_name,
'element_id': self.element_id,
'element_name': self.element_name,
'element_category': self.element_category,
'severity': self.severity.value,
'message': self.message
}
@dataclass
class ValidationReport:
"""Complete validation report."""
project_name: str
model_name: str
validated_at: datetime
status: ValidationStatus
total_elements: int
elements_with_issues: int
issues: List[ValidationIssue]
rules_checked: int
summary_by_severity: Dict[str, int]
summary_by_category: Dict[str, int]
class BIMValidationEngine:
"""BIM model validation engine."""
def __init__(self, project_name: str, model_name: str):
self.project_name = project_name
self.model_name = model_name
self.rules: List[ValidationRule] = []
self.issues: List[ValidationIssue] = []
self._issue_counter = 0
# Load default rules
self._load_default_rules()
def _load_default_rules(self):
"""Load standard validation rules."""
# Required properties rules
self.add_rule(ValidationRule(
rule_id="REQ-001",
name="Element Name Required",
category=RuleCategory.REQUIRED_PROPERTIES,
description="All elements must have a name",
severity=ValidationSeverity.ERROR,
check_function=lambda e: bool(e.get('name'))
))
self.add_rule(ValidationRule(
rule_id="REQ-002",
name="Level Assignment Required",
category=RuleCategory.REQUIRED_PROPERTIES,
description="Elements must be assigned to a level",
severity=ValidationSeverity.WARNING,
check_function=lambda e: bool(e.get('level')),
applicable_categories=["Walls", "Floors", "Doors", "Windows"]
))
self.add_rule(ValidationRule(
rule_id="REQ-003",
name="Material Required",
category=RuleCategory.REQUIRED_PROPERTIES,
description="Structural elements must have material defined",
severity=ValidationSeverity.ERROR,
check_function=lambda e: bool(e.get('material')),
applicable_categories=["Structural Columns", "Structural Framing", "Floors"]
))
# Naming convention rules
self.add_rule(ValidationRule(
rule_id="NAM-001",
name="No Special Characters",
category=RuleCategory.NAMING_CONVENTION,
description="Names should not contain special characters",
severity=ValidationSeverity.WARNING,
check_function=self._check_no_special_chars
))
self.add_rule(ValidationRule(
rule_id="NAM-002",
name="Name Length Check",
category=RuleCategory.NAMING_CONVENTION,
description="Names should be between 3 and 100 characters",
severity=ValidationSeverity.INFO,
check_function=lambda e: 3 <= len(e.get('name', '')) <= 100
))
# Classification rules
self.add_rule(ValidationRule(
rule_id="CLS-001",
name="Classification Code Present",
category=RuleCategory.CLASSIFICATION,
description="Elements should have classification code",
severity=ValidationSeverity.WARNING,
check_function=lambda e: bool(e.get('classification_code') or e.get('uniformat'))
))
# Geometric rules
self.add_rule(ValidationRule(
rule_id="GEO-001",
name="Non-Zero Volume",
category=RuleCategory.GEOMETRIC,
description="3D elements must have non-zero volume",
severity=ValidationSeverity.ERROR,
check_function=lambda e: float(e.get('Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
Manage as-built documentation for project closeout. Track drawing markups, coordinate updates, and verify completeness.
Track as-built documentation and record drawings. Monitor submission status, manage revisions, and ensure completeness for handover.
Batch convert multiple CAD/BIM files (Revit, IFC, DWG, DGN) with progress tracking, error handling, and consolidated reporting.
Compare and analyze contractor bids. Score proposals, identify scope gaps, and recommend selections.
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
Automated cost estimation from BIM models using DDC CWICR database (8 national bases, 78,228 positions). AI classification + vector search for accurate pricing.