README.md

metadata

title: Enhanced Concrete Creep Prediction
emoji: 🏗️
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.0
app_file: app.py
pinned: false
license: mit

🏗️ Enhanced Concrete Creep Prediction

This Hugging Face Space provides concrete creep strain prediction using an enhanced LLM-style model with advanced feature processing.

🚀 Features

• Enhanced LLM-Style Architecture: Feature-wise projection, parallel attention mechanisms, and hybrid token pooling
• Autoregressive Prediction: Step-by-step prediction generation for high accuracy
• Real-time Inference: Fast prediction with detailed timing metrics
• Interactive Interface: Easy-to-use Streamlit interface with comprehensive visualization

🔧 Model Architecture

Enhanced Features:

• Feature-wise projection: Each feature (Density, fc, E) is projected to 16-dimensional vectors
• Parallel attention mechanisms:
  • Feature-wise attention with 4 heads on 16-dim embeddings
  • Batch-wise attention with 4 heads on 16-dim embedding
• Hybrid token pooling: Combines mean, attention, and last token pooling methods
• Autoregressive prediction: Generates predictions step by step for accuracy

Technical Specifications:

• Layers: 4 transformer layers
• Attention Heads: 4 heads per layer
• Model Dimension: 192 (d_model)
• Feed Forward: 768 dimensions (4 × d_model)
• Parameters: ~750K total parameters
• Dropout: 0.057

📊 Usage

1. Input Parameters: Enter concrete properties in the sidebar:

   • Density (kg/m³): 2000-3000
   • Compressive Strength (fc) in MPa: 10-1000
   • Elastic Modulus (E) in MPa: 10,000-1,000,000
   • Initial Creep Value: Usually 0

2. Time Settings: Configure prediction timeframe:

   • Maximum Time (days): Up to 10,000 days
   • Use Original Time Values: Recommended for best accuracy

3. Generate Prediction: Click "🚀 Predict Creep Strain" to get results

📈 Output Features

• Interactive Plots: Linear and log-scale visualization of creep development
• Detailed Metrics: Comprehensive timing and performance statistics
• Data Export: Download predictions as CSV files
• Summary Statistics: Key metrics including creep rates and ranges

⚡ Performance

• Inference Speed: ~0.1-1.0 seconds for 1000 time points
• Memory Usage: ~500MB RAM
• GPU Acceleration: Automatic detection and usage when available
• Model Efficiency: Optimized for cloud deployment

🔬 Research Background

This model represents an advanced approach to concrete creep prediction using transformer-based architecture adapted for time series forecasting. The enhanced feature processing and attention mechanisms allow for better capture of complex relationships in concrete behavior over time.

Key Innovations:

• Application of LLM-style attention to concrete engineering
• Parallel processing of features and temporal sequences
• Hybrid pooling for comprehensive representation
• Autoregressive generation for reliable long-term predictions

🛠️ Technical Details

The model uses PyTorch for deep learning computations and Streamlit for the interactive interface. All predictions are performed in real-time with comprehensive error handling and performance monitoring.

📝 Citation

If you use this model or interface in your research, please cite the relevant papers and acknowledge this implementation.

🤝 Support

For technical questions or issues, please refer to the original research documentation or create an issue in the source repository.

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Enhanced Concrete Creep Prediction
Powered by LLM-Style Model with Advanced Feature Processing
Deployed on Hugging Face Spaces