{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:16:41Z","timestamp":1768990601961,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019YFC0605100"],"award-info":[{"award-number":["2019YFC0605100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Key R&amp;D Program of China","award":["2019YFC0605103"],"award-info":[{"award-number":["2019YFC0605103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51778476"],"award-info":[{"award-number":["51778476"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51978431"],"award-info":[{"award-number":["51978431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52038008"],"award-info":[{"award-number":["52038008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Development Funds","award":["grant 20DZ1202004"],"award-info":[{"award-number":["grant 20DZ1202004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22062412","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"2412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis"],"prefix":"10.3390","volume":"22","author":[{"given":"Yonglai","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, School of Civil Engineering, Tongji University, Shanghai 200092, China"}]},{"given":"Xiongyao","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, School of Civil Engineering, Tongji University, Shanghai 200092, China"}]},{"given":"Hongqiao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"China Shipbuilding NDRI Engineering Co., Ltd., Shanghai 200090, China"}]},{"given":"Biao","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, School of Civil Engineering, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","first-page":"8","article-title":"Research and practice of smart sensor networks and health monitoring systems for civil infrastructures in mainland China","volume":"19","author":"Ou","year":"2005","journal-title":"Bull. 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