使用对抗和多任务学习在桥梁之间进行知识转移以进行驾驶监控
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning
摘要：
利用驶过车辆的振动监测桥梁健康状况具有各种好处，例如成本低，不需要在桥梁上直接安装或现场维护设备。然而，许多这样的方法需要来自每个网桥的标记数据，这是昂贵且耗时的，如果不是不可能的话。这进一步加剧了具有多个诊断任务，如损伤量化和定位。解决这个问题的一种方法是将为一座桥梁训练的监督模型直接应用于其他桥梁，尽管这可能会由于不同桥梁数据之间的分布失配而显著降低准确性。为了缓解这些问题，我们引入了一个迁移学习框架，使用领域对抗训练和多任务学习来检测、定位和量化损伤。具体来说，我们以对抗的方式训练一个深度网络，以学习1）对损伤敏感和2）对不同桥梁不变的特征。此外，为了改善从一个任务到下一个任务的错误传播，我们的框架使用多任务学习来学习所有任务的共享特征。我们评估我们的框架使用实验室规模的实验与两个不同的桥梁。平均而言，我们的框架分别实现了94%，97%和84%的准确度，损伤检测，定位和量化。在一个严重程度的损害。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
