School of Civil Engineering, Tongji University, Shanghai 200092, China.
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, School of Civil Engineering, Tongji University, Shanghai 200092, China.
Sensors (Basel). 2022 Mar 21;22(6):2412. doi: 10.3390/s22062412.
Finding a low-cost and highly efficient method for identifying subway tunnel damage can greatly reduce catastrophic accidents. At present, tunnel health monitoring is mainly based on the observation of apparent diseases and vibration monitoring, which is combined with a manual inspection to perceive the tunnel health status. However, these methods have disadvantages such as high cost, short working time, and low identification efficiency. Thus, in this study, a tunnel damage identification algorithm based on the vibration response of in-service train and WPE-CVAE is proposed, which can automatically identify tunnel damage and give the damage location. The method is an unsupervised novelty detection that requires only sufficient normal data on healthy structure for training. This study introduces the theory and implementation process of this method in detail. Through laboratory model tests, the damage of the void behind the tunnel wall is designed to verify the performance of the algorithm. In the test case, the proposed method achieves the damage identification performance with a 96.25% recall rate, 86.75% hit rate, and 91.5% accuracy. Furthermore, compared with the other unsupervised methods, the method performance and noise immunity are better than others, so it has a certain practical value.
找到一种低成本、高效率的地铁隧道损伤识别方法，可以大大减少灾难性事故的发生。目前，隧道健康监测主要基于表观病害的观察和振动监测，并结合人工检测来感知隧道的健康状况。然而，这些方法存在成本高、工作时间短、识别效率低等缺点。因此，本研究提出了一种基于运行中列车振动响应和 WPE-CVAE 的隧道损伤识别算法，可以自动识别隧道损伤并给出损伤位置。该方法是一种无监督的新颖性检测方法，仅需要对健康结构进行足够的正常数据训练。本研究详细介绍了该方法的原理和实现过程。通过实验室模型试验，设计了隧道壁后空洞的损伤，验证了算法的性能。在测试案例中，所提出的方法实现了 96.25%的召回率、86.75%的命中率和 91.5%的准确率的损伤识别性能。此外，与其他无监督方法相比，该方法的性能和抗噪能力优于其他方法，因此具有一定的实用价值。
