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🔍 Master's Thesis project on using machine learning as part of a Structural Health Monitoring (SHM) assessment on a damaged, post-tensioned bridge. This is an open-source file repository for the master's project "Machine Learning-Assisted Structural Health Monitoring of Herøysund Bridge". Models and datasets can be provided upon request.

For more information or to request models and datasets, please contact:

• Erling Husøy at [email protected]
• Emil Steen at [email protected]

We will soon be unavailable through NTNU's email system, but you can reach us at the provided addresses.

To run the code using TensorFlow-GPU, ensure that your system meets the following requirements:

1. CUDA-Capable Graphics Card: Ensure that you have a compatible NVIDIA graphics card installed in your system.

2. Required NVIDIA® Software:

   • NVIDIA® GPU drivers version 450.80.02 or higher.
     • Note: Can be checked with the command nvidia-smi in PowerShell.
   • CUDA® Toolkit 11.8.
   • cuDNN SDK 8.6.0.

Without these requirements, TensorFlow-GPU will not be able to utilize the GPU for computations, and the code may fall back to CPU execution, leading to slower performance.

In order to take use of the GPU, follow dowload instructions from: https://learn.microsoft.com/en-us/windows/wsl/install Command in powershell: wsl --install

NOTICE: Please note that the build of this repository is around Python 3.11.7.

1. Setting Up a Virtual Environment:

Before you start, it's recommended to set up a virtual environment. This ensures that the dependencies of the project don't interfere with your system Python and other projects.

For Windows:

python -m venv venv venv\Scripts\activate

You should now see (venv) at the beginning of your terminal prompt, indicating that you're in the virtual environment.

2. Installing Dependencies:

With the virtual environment activated, install the project's dependencies using the following command:

pip install -r requirements.txt

This will install all required packages listed in the requirements.txt file.

3. Deactivating the Virtual Environment:

When you're done, you can deactivate the virtual environment and return to your system Python with:

deactivate

py -3.11 -m venv venv .\venv\Scripts\activate py --version py -m pip install -U pip wheel py -m pip install -r requirements.txt

From TensorFlow-docs: Verify the installation Verify the GPU setup: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" If a list of GPU devices is returned, you've installed TensorFlow success