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
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.
git clone https://github.com/jingxiaoliu/HierMUD.git
cd HierMUD
- Run the digit recognition example with
jupyter notebook demo_mnist.ipynb
Feel free to send any questions to:
- Jingxiao Liu, Ph.D. Candidate at Stanford University, Department of Civil and Environmental Engineering.
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