README.md

---
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*