This repository contains code samples and notebooks illustrating the application of machine learning in civil and structural engineering. The first notebook is for predicting concrete strength. New Notebooks will be added periodically as a I develop them at work. The code herein serves as a practical introduction to machine learning in the engineering domain.
- 🔩 Linear Regression Model for Concrete Strength Prediction
- 🧠 Deep Learning Model for Concrete Strength Prediction
For a deep dive into the concepts and applications covered in this repository, check out the corresponding post on my Substack.
- Clone the repo
- Install requirements:
pip install -r requirements.txt - Run Jupyter Notebook:
jupyter notebook
If you prefer to experiment with the notebooks without installing anything on your local machine, consider using one of these cloud-based platforms:
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Google Colab: A free, cloud-based version of Jupyter Notebook. Great for experimenting with the notebooks without any setup. Simply upload the notebook files to Colab. Google Colab.
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Kaggle Kernels: Provides a cloud-based Jupyter Notebook environment. It also offers free access to GPUs. Useful for trying out the notebooks and for more intensive computations. Kaggle Kernels.
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GitHub Codespaces: An integrated development environment within GitHub that allows you to work with repositories in a cloud-based VS Code interface. It's a perfect way to experiment with the code if you're already comfortable with GitHub. GitHub Codespaces.
Feel free to fork the repository and make contributions. For any bugs or feature requests, please open an issue.