论文标题
桥梁之间的知识转移，用于使用对抗和多任务学习的驾驶监控
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning
论文作者
论文摘要
使用车辆的振动监测桥梁健康具有各种好处，例如低成本，无需直接安装或在桥上进行设备的现场维护。但是，许多这样的方法都需要每个桥的标记数据，这是昂贵且耗时的（即使不是不可能）的。通过具有多个诊断任务（例如损害量化和本地化），这进一步加剧了这一点。解决此问题的一种方法是直接将一座桥梁训练的监督模型应用于另一座桥梁，尽管由于不同的桥梁之间的分配不匹配，这可能会大大降低准确性。为了减轻这些问题，我们使用域交流训练和多任务学习引入了转移学习框架，以检测，本地化和量化损害。具体来说，我们以对抗性方式训练一个深层网络，以学习1）对损害敏感的功能，而2）对不同的桥梁不变。此外，为了改善从一个任务到另一个任务的错误传播，我们的框架使用多任务学习学习了所有任务的共享功能。我们使用两个不同桥梁的实验室规模实验来评估我们的框架。 On average, our framework achieves 94%, 97% and 84% accuracy for damage detection, localization and quantification, respectively.在一个伤害严重程度下。
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.
