5000-projects-analysis

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---
name: "5000-projects-analysis"
description: "Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw": {"emoji": "📓", "os": ["win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
---
# Large-Scale BIM Project Analysis

## Business Case

### Problem Statement
Construction companies lack industry benchmarks because:
- Individual project data is insufficient for statistical analysis
- Comparable project data is not available
- Manual analysis doesn't scale to thousands of projects

### Solution
Analyze 5000+ IFC and Revit projects to extract patterns, create benchmarks, and train ML models for prediction.

### Business Value
- **Industry benchmarks** - Compare your project to 5000+ others
- **Pattern detection** - Identify common designs and issues
- **ML training data** - Build predictive models with real data
- **Research foundation** - Academic and industry research dataset

## Technical Implementation

### Dataset Overview
| Metric | Value |
|--------|-------|
| Total Projects | 5000+ |
| File Formats | IFC, RVT |
| Elements | Millions |
| Categories | 200+ |

### Analysis Pipeline

```python
import pandas as pd
import numpy as np
from pathlib import Path
from typing import Dict, List
import matplotlib.pyplot as plt
import seaborn as sns

class BIMProjectAnalyzer:
    def __init__(self, data_path: str):
        self.data_path = Path(data_path)
        self.projects = []
        self.elements = None

    def load_projects(self) -> int:
        """Load all project data."""
        project_files = list(self.data_path.glob("*.xlsx"))

        for f in project_files:
            try:
                df = pd.read_excel(f, sheet_name="Elements")
                df['ProjectId'] = f.stem
                self.projects.append(df)
            except Exception as e:
                print(f"Error loading {f}: {e}")

        self.elements = pd.concat(self.projects, ignore_index=True)
        return len(self.projects)

    def project_statistics(self) -> pd.DataFrame:
        """Calculate statistics per project."""
        stats = self.elements.groupby('ProjectId').agg({
            'ElementId': 'count',
            'Category': 'nunique',
            'Volume': ['sum', 'mean'],
            'Area': ['sum', 'mean']
        }).reset_index()

        stats.columns = [
            'ProjectId', 'ElementCount', 'CategoryCount',
            'TotalVolume', 'AvgVolume', 'TotalArea', 'AvgArea'
        ]
        return stats

    def category_distribution(self) -> pd.DataFrame:
        """Analyze element distribution across categories."""
        dist = self.elements.groupby('Category').agg({
            'ElementId': 'count',
            'ProjectId': 'nunique',
            'Volume': 'sum',
            'Area': 'sum'
        }).reset_index()

        dist.columns = ['Category', 'ElementCount', 'ProjectCount',
                        'TotalVolume', 'TotalArea']
        dist['AvgPerProject'] = dist['ElementCount'] / dist['ProjectCount']

        return dist.sort_values('ElementCount', ascending=False)

    def find_outliers(self, column: str, threshold: float = 3.0) -> pd.DataFrame:
        """Find projects with outlier values."""
        stats = self.project_statistics()
        mean = stats[column].mean()
        std = stats[column].std()

        z_scores = np.abs((stats[column] - mean) / std)
        outliers = stats[z_scores > threshold]

        return outliers

    def benchmark_project(self, project_id: str) -> Dict:
        """Compare project against dataset benchmarks."""
        stats = self.project_statistics()
        project = stats[stats['ProjectId'] == project_id].iloc[0]

        percentiles = {}
        for col in ['ElementCount', 'TotalVolume', 'TotalArea']:
            percentile = (stats[col] < project[col]).mean() * 100
            percentiles[col] = round(per