5000-projects-analysis

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":"[Truncated]"}}}

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

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 (.projects)

     () -> pd.DataFrame:
        
        stats = .elements.groupby().agg({
            : ,
            : ,
            : [, ],
            : [, ]
        }).reset_index()

        stats.columns = [
            , , ,
            , , , 
        ]
         stats

     () -> pd.DataFrame:
        
        dist = .elements.groupby().agg({
            : ,
            : ,
            : ,
            : 
        }).reset_index()

        dist.columns = [, , ,
                        , ]
        dist[] = dist[] / dist[]

         dist.sort_values(, ascending=)

     () -> pd.DataFrame:
        
        stats = .project_statistics()
        mean = stats[column].mean()
        std = stats[column].std()

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

         outliers

     () -> :
        
        stats = .project_statistics()
        project = stats[stats[] == project_id].iloc[]

        percentiles = {}
         col  [, , ]:
            percentile = (stats[col] < project[col]).mean() * 
            percentiles[col] = (percentile, )

         {
            : project_id,
            : percentiles,
            : {
                col: project[col] > stats[col].mean()
                 col  [, , ]
            }
        }

     () -> :
        
        stats = .project_statistics()
        cat_dist = .category_distribution()

        
        fig, axes = plt.subplots(, , figsize=(, ))

        
        axes[, ].hist(stats[], bins=, edgecolor=)
        axes[, ].set_title()
        axes[, ].set_xlabel()

        
        top_cats = cat_dist.head()
        axes[, ].barh(top_cats[], top_cats[])
        axes[, ].set_title()

        
        axes[, ].hist(stats[], bins=, edgecolor=)
        axes[, ].set_title()

        
        axes[, ].scatter(stats[], stats[], alpha=)
        axes[, ].set_xlabel()
        axes[, ].set_ylabel()
        axes[, ].set_title()

        plt.tight_layout()
        plt.savefig(output_path, dpi=)

         output_path

Analysis Examples

# Initialize analyzer
analyzer = BIMProjectAnalyzer("C:/Data/5000_Projects")

# Load all projects
num_projects = analyzer.load_projects()
print(f"Loaded {num_projects} projects")

# Get statistics
stats = analyzer.project_statistics()
print("\nDataset Summary:")
print(f"  Total elements: {analyzer.elements.shape[0]:,}")
print(f"  Avg elements/project: {stats['ElementCount'].mean():,.0f}")
print(f"  Avg volume/project: {stats['TotalVolume'].mean():,.2f} m³")

# Category analysis
categories = analyzer.category_distribution()
print("\nTop 10 Categories:")
print(categories.head(10)[['Category', 'ElementCount', 'AvgPerProject']])

# Benchmark a specific project
benchmark = analyzer.benchmark_project("MyProject_001")
print(f"\nProject Benchmark:")
print(f"  Element count: {benchmark['percentiles']['ElementCount']}th percentile")
print(f"  Total volume: {benchmark['percentiles']['TotalVolume']}th percentile")

# Generate report
report_path = analyzer.generate_report("analysis_report.png")

Insights You Can Extract

Structural Patterns

• Average wall-to-floor ratio
• Typical door/window counts per area
• MEP element density benchmarks

Quality Indicators

• Category completeness
• Parameter fill rates
• Geometric consistency

Complexity Metrics

• Elements per m² of floor area
• Category diversity index
• Level count vs building height

Integration with ML

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

# Prepare features for cost prediction
features = stats[[
    'ElementCount', 'CategoryCount',
    'TotalVolume', 'TotalArea'
]].values

# Assuming you have cost data
# costs = [project_cost_data]

# Train model
X_train, X_test, y_train, y_test = train_test_split(
    features, costs, test_size=0.2
)

model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)

# Predict for new project
new_project = [[5000, 50, 15000, 8000]]  # elements, categories, volume, area
predicted_cost = model.predict(new_project)

Resources

• Kaggle Notebook: 5000 Projects Analysis
• Dataset: Available via DataDrivenConstruction.io