Large-Span Bridge Strain Reconstruction Based on Bidirectional LSTM and ESN

Yan Ke Tan, Yu Ling Wang, Yi Qing Ni, Qi Lin Zhang, You Wu Wang

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

Abstract

Partly missing and anomalous of the data collected from structural health monitoring (SHM) systems are inevitable due to the failure of sensor and data acquisition equipment, which lead to misjudgment of the target structure state. The data integrity demands for guaranty using reconstruction algorithms before signal processing. Recurrent neural networks (RNN) has been proved effective of reconstruction issue by learning from the historical and future signal segments. The gated RNN represented by long short-term memory (LSTM) networks and reservoir computing represented by echo state networks (ESNs) show superiority on accuracy or training efficiency than standard RNN method. In addition, bidirectional concept can be introduced into these two methods to further improve their reconstruction precision. In this paper, models built by bidirectional LSTM and ESN are used to reconstruct strain data measured by the SHM system of Tsing Kau bridge, during which performances in both time and frequency domains are compared and evaluated. Furthermore, hyperparameters including number of layers, number of hidden units, scale of reservoir, and leaky rate have been optimized to improve the structures of the proposed models.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2023
Subtitle of host publicationDesigning SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
EditorsSaman Farhangdoust, Alfredo Guemes, Fu-Kuo Chang
PublisherDEStech Publications
Pages2870-2878
Number of pages9
ISBN (Electronic)9781605956930
Publication statusPublished - 2023
Event14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023 - Stanford, United States
Duration: 12 Sept 202314 Sept 2023

Publication series

NameStructural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring

Conference

Conference14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023
Country/TerritoryUnited States
CityStanford
Period12/09/2314/09/23

ASJC Scopus subject areas

  • Computer Science Applications
  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality
  • Building and Construction

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