# HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis

arXiv: https://arxiv.org/abs/2107.11435 · HTML: https://mlsanity.com/paper/2107.11435/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [The Benefit of Multitask Representation Learning](https://mlsanity.com/paper/1505.06279/) (2015)
- [Multi-task Sequence to Sequence Learning](https://mlsanity.com/paper/1511.06114/) (2015)
- [Deep Multi-task Representation Learning: A Tensor Factorisation Approach](https://mlsanity.com/paper/1605.06391/) (2016)
- [A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://mlsanity.com/paper/1611.01587/) (2016)
- [Transfer Adaptation Learning: A Decade Survey](https://mlsanity.com/paper/1903.04687/) (2019)
- [Generalization Error in Deep Learning](https://mlsanity.com/paper/1808.01174/) (2018)
- [Unsupervised Domain Adaptation via Discriminative Manifold Propagation](https://mlsanity.com/paper/2008.10030/) (2020)
- [Deep Cross Residual Learning for Multitask Visual Recognition](https://mlsanity.com/paper/1604.01335/) (2016)
- [Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces](https://mlsanity.com/paper/1802.09913/) (2018)
- [Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning](https://mlsanity.com/paper/2002.09513/) (2020)
- [Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring](https://mlsanity.com/paper/2002.02105/) (2020)
- [Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning](https://mlsanity.com/paper/2006.03641/) (2020)

## Built on by (most cited first, 2 of 2)

- [Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection](https://mlsanity.com/paper/2307.07643/) (2023)
- [Leveraging Audio Representations for Vibration-Based Crowd Monitoring in Stadiums](https://mlsanity.com/paper/2503.17646/) (2025)

## Abstract (arXiv)

Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge. However, many of the existing drive-by monitoring approaches are based on supervised learning models that require labeled data from every bridge of interest, which is expensive and time-consuming, if not impossible, to obtain. To this end, we introduce a new framework that transfers the model learned from one bridge to diagnose damage in another bridge without any labels from the target bridge. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features that are informative to multiple diagnostic tasks and invariant across multiple bridges. We evaluate our framework on experimental data collected from 2 bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93% for localization, and up to 72% for quantification, which are ~2 times improvements from baseline methods.