Smart Structure Guardian

This project simulates sensor data for a bridge structure and uses machine learning to detect anomalies. The project includes data simulation, preprocessing, model training, anomaly detection, and visualization.

Table of Contents

• Project Structure
• Setup
• Usage
• Visualization
• Contributing
• License
• Author

Project Structure

SmartStructureGuardian
│
├── data
│   └── raw
│   └── processed
│
├── notebooks
│   └── data_simulation.ipynb
│   └── data_analysis.ipynb
│   └── anomaly_detection.ipynb
│
├── src
│   ├── data_simulation.py
│   ├── data_preprocessing.py
│   ├── model_training.py
│   ├── anomaly_detection.py
│   └── visualization.py
│
├── docs
│   ├── README.md
│   ├── setup.md
│   └── usage.md
│
├── models
│   └── trained_model.h5
│
├── outputs
│   ├── figures
│   └── reports
│
└── requirements.txt

Setup

1. Clone the repository:

   git clone https://github.com/oaslananka/SmartStructureGuardian.git
   cd SmartStructureGuardian

2. Create and activate a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install the required Python packages:

   pip install -r requirements.txt

Usage

1. Simulate sensor data:

   python src/data_simulation.py

   This will generate simulated sensor data and save it to data/raw/simulated_sensor_data.csv.

2. Preprocess the sensor data:

   python src/data_preprocessing.py

   This will preprocess the sensor data and save it to data/processed/preprocessed_sensor_data.csv.

3. Train the anomaly detection model:

   python src/model_training.py

   This will train the anomaly detection model and save it to models/anomaly_detection_model.pkl.

4. Detect anomalies in the sensor data:

   python src/anomaly_detection.py

   This will detect anomalies in the sensor data and save the results to outputs/reports/anomaly_detection_results.csv.

5. Visualize the anomalies:

   python src/visualization.py

   This will generate a 3D animated GIF showing the bending and deformation of the bridge and save it to outputs/figures/bridge_bending_animation.gif.

Visualization

The project includes scripts to visualize the sensor data and detected anomalies:

• visualization.py: Creates a 3D animation of the bridge deformation based on the stress data and saves it as a GIF.

Example visualization of sensor data and detected anomalies:

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Author

This project was created by oaslananka.