Related papers: Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning

Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning

URL: http://arxiv.org/abs/2006.03641v1
Date: Fri, 5 Jun 2020 19:18:45 GMT
Title: Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning
Authors: Jingxiao Liu, Mario Berg\'es, Jacobo Bielak, Hae Young Noh
Abstract summary: Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. Many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impossible, to obtain. We introduce a transfer learning framework using domain-adversarial training and multi-task learning to detect, localize and quantify damage.
Score: 6.462702225377603
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Monitoring bridge health using the vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. However, many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impossible, to obtain. This is further exacerbated by having multiple diagnostic tasks, such as damage quantification and localization. One way to address this issue is to directly apply the supervised model trained for one bridge to other bridges, although this may significantly reduce the accuracy because of distribution mismatch between different bridges'data. To alleviate these problems, we introduce a transfer learning framework using domain-adversarial training and multi-task learning to detect, localize and quantify damage. Specifically, we train a deep network in an adversarial way to learn features that are 1) sensitive to damage and 2) invariant to different bridges. In addition, to improve the error propagation from one task to the next, our framework learns shared features for all the tasks using multi-task learning. We evaluate our framework using lab-scale experiments with two different bridges. On average, our framework achieves 94%, 97% and 84% accuracy for damage detection, localization and quantification, respectively. within one damage severity level.

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