The paper titled “An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures” by Matteo Vagnoli (ESR9) and his supervisor Rasa Remenyte-Prescott was published in the 2018 November issue, volume 81 of Tunnelling and Underground Space Technology. The latter falls within the top quartile of journal publications in the building and construction subject. The paper can be read at the DOI link of the publisher’s website provided with the full reference below.
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 are 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 the infrastructure condition is provided to decision makers.
Keywords: Structural health monitoring; SHM; data mining; change-point detection; ensemble of change-point detection methods.
Tunnelling and Underground Space Technology (ISSN: 0886-7798), from Elsevier, publishes authoritative articles encompassing the development of innovative uses of underground space and the results of high quality research into improved, more cost-effective techniques for the planning, geo-investigation, design, construction, operation and maintenance of underground and earth-sheltered structures. According to scimagojr, the journal has the following impact indicators: