An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures

Vagnoli, Matteo; Remenyte-Prescott, Rasa

Abstract

A large amount of data is generated by Structural Health Monitoring (SHM) systems and, as a consequence, processing and interpreting this data can be difficult and time consuming. Particularly, if work activities such as maintenance or modernization are carried out on a bridge or tunnel infrastructure, a robust data analysis is needed, in order to accurately and quickly process the data and provide reliable information to decision makers. In this way the service disruption can be minimized and the safety of the asset and the workforce guaranteed.

In this paper a data mining method for detecting critical behaviour of a railway tunnel is presented. The method starts with a pre-processing step that aims to remove the noise in the recorded data. A feature definition and selection step is then performed to identify the most critical area of the tunnel. An ensemble of change-point detection algorithms is proposed, in order to analyse the critical area of the tunnel and point out the time when unexpected behaviour occurs, as well as its duration and location. The work activities, which are carried out at the time of occurrence of the critical behaviour and have caused this behaviour, are finally identified from a database of the work schedule and used for the validation of the results. Using the proposed method, fast and reliable information about infrastructure condition is provided to decision makers.

Citation

Vagnoli, M., & Remenyte-Prescott, R. (2018). An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures. Tunnelling and Underground Space Technology, 81, 68-82. https://doi.org/10.1016/j.tust.2018.07.013

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2018
• Online Publication Date: Jul 10, 2018
• Publication Date: Nov 30, 2018
• Deposit Date: Sep 20, 2018
• Publicly Available Date: Jul 11, 2019
• Journal: Tunnelling and Underground Space Technology
• Print ISSN: 0886-7798
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 81
• Pages: 68-82
• DOI: https://doi.org/10.1016/j.tust.2018.07.013
• Keywords: Structural Health Monitoring (SHM); Data mining; Change-point detection; Ensemble of change-point detection methods
• Public URL: https://nottingham-repository.worktribe.com/output/1093819
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0886779817306454
• Additional Information: This article is maintained by: Elsevier; Article Title: An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures; Journal Title: Tunnelling and Underground Space Technology; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.tust.2018.07.013; Content Type: article; Copyright: © 2018 Elsevier Ltd. All rights reserved.