Smart Structures and Systems

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Towards high-accuracy data modelling, uncertainty quantification and correlation analysis for SHM measurements during typhoon events using an improved most likely heteroscedastic Gaussian process

  • Qi-Ang Wang (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Hao-Bo Wang (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Zhan-Guo Ma (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Yi-Qing Ni (National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch) and Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Zhi-Jun Liu (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Jian Jiang (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Rui Sun (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology) ;
  • Hao-Wei Zhu (State Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering & School of Mechanics and Civil Engineering, China University of Mining and Technology)
  • Received : 2022.12.01
  • Accepted : 2023.10.12
  • Published : 2023.10.25
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Abstract

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

Keywords

Acknowledgement

The study was supported by the National Key Research and Development Program of China under Award Number 2019YFE0118500, China Postdoctoral Science Foundation (2019M652006) and the National Natural Science Foundation of China (NSFC) under Award Numbers (51708545 and 52078478). The authors wish to express their gratitude to the staff and students in the Structural Engineering Laboratory for their extensive assistance. The data used to support the findings of this study are available from the corresponding author upon request.

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