Subway tunnel damage detection based on in-service train dynamic response, variational mode decomposition, convolutional neural networks and long short-term memory
Authors:
- Yonglai ZhangYZYonglai Zhang
- Xiongyao XieXXXiongyao Xie
- Hongqiao LiHLHongqiao Li
- Biao ZhouBZBiao Zhou
- Qiang WangQWQiang Wang
- Isam ShahrourISIsam Shahrour
- 2 May 2022
- journal article
- research article
- Published by Elsevier in Automation in Construction
- Vol. 139
Abstract
No abstract availableKeywords
Funding Information
- Shanghai Science and Technology Development Foundation (20DZ1202004)
- National Natural Science Foundation of China (51778476, 52038008)
- National Key Research and Development Program of China (2019YFC0605100, 2019YFC0605103)
This publication has 36 references indexed in Scilit:
- Track monitoring from the dynamic response of a passing train: A sparse approachMechanical Systems and Signal Processing, 2017
- Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithmStructural Health Monitoring, 2017
- Real-time vibration-based structural damage detection using one-dimensional convolutional neural networksJournal of Sound and Vibration, 2017
- Urban rail track condition monitoring based on in-service vehicle acceleration measurementsMeasurement, 2016
- Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy PricesIEEE Systems Journal, 2015
- A Review of Indirect Bridge Monitoring Using Passing VehiclesShock and Vibration, 2015
- Variational Mode DecompositionIEEE Transactions on Signal Processing, 2013
- The effect of weak interlayer on the failure pattern of rock mass around tunnel – Scaled model tests and numerical analysisTunnelling and Underground Space Technology, 2013
- A review on recent development of vibration-based structural robust damage detectionSTRUCTURAL ENGINEERING AND MECHANICS, 2013
- Measurement of railhead longitudinal profiles: a comparison of different techniquesWear, 1996