A heteroscedastic Gaussian process approach for SHM-based modelling and forecasting of high-speed rail track slab deformation

Q. A. Wang, Y. Q. Ni, C. Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Uncertainty complicates structural health monitoring (SHM) data modelling and forecasting for high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, this assumption can be unrealistic for HSR SHM data modelling because of its unique heteroscedastic uncertainty induced by dynamic train loading, electromagnetic interference, large temperature variation and daily maintenance of railway track infrastructure. This study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, a heteroscedastic GP approach, Variational Heteroscedastic Gaussian Process (VHGP), is explored for data modelling, estimation of the monitoring data uncertainty level and data forecasting. Results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be forecasted more accurately.

Original languageEnglish
Title of host publication9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Subtitle of host publicationTransferring Research into Practice, SHMII 2019 - Conference Proceedings
EditorsGenda Chen, Sreenivas Alampalli
PublisherInternational Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
Pages454-459
Number of pages6
ISBN (Electronic)9780000000002
Publication statusPublished - 2019
Event9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - St. Louis, United States
Duration: 4 Aug 20197 Aug 2019

Publication series

Name9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings
Volume1

Conference

Conference9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019
CountryUnited States
CitySt. Louis
Period4/08/197/08/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Civil and Structural Engineering
  • Building and Construction

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