HierMUD: a Hierarchical Multi-task Unsupervised Domain adaptation framework

This is the repository for the paper:

• Jingxiao Liu, Susu Xu, Mario Bergés, Hae Young Noh. HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis. Structural Health Monitoring. August 2022. doi:10.1177/14759217221081159

[paper]

Description

We introduce HierMUD, a novel approach for multi-task unsupervised domain adaptation. This approach is developed for bridge health monitoring using drive-by vehicle vibrations, but it can be applied to other problems, such as digit recognition, image classification, etc.

In this repository, we demonstrate our approach through two examples:

• A drive-by bridge health monitoring example, which transfers model learned using vehicle vibration data collected from one bridge to detect, localize and quantify damage on another bridge.
• A digit recognition example, which transfers model learned using MNIST data to MNIST-M data and conducts two tasks: odd-even classification and digits comparison.

Note: the drive-by bridge health monitoring experiment involves data that is not publicly available. We will work towards making the experiment replicable without violating data usage policy.

Code Usage

git clone https://github.com/jingxiaoliu/HierMUD.git
cd HierMUD

• Run the digit recognition example with

jupyter notebook demo_mnist.ipynb

Contact

Feel free to send any questions to:

• Jingxiao Liu, Ph.D. Candidate at Stanford University, Department of Civil and Environmental Engineering.

Citation

If you use this implementation, please cite our paper as follows:

Liu J, Xu S, Bergés M, Noh HY. HierMUD: Hierarchical multi-task unsupervised domain adaptation between bridges for drive-by damage diagnosis. Structural Health Monitoring. August 2022. doi:10.1177/14759217221081159