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Hydrostatic-season-time model updating using Bayesian model class selection

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  • Gamse, Sonja
  • Zhou, Wan-Huan
  • Tan, Fang
  • Yuen, Ka-Veng
  • Oberguggenberger, Michael

Abstract

The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-season-time (HST) model. The empirical HST-model has been widely used for the analysis of different measurement data types on dams. The significance of individual parameters or their sub-groups for modelling the influence of the water level, air and water temperature, and irreversible deformations due to the ageing of the dam, depends on the structure itself. The process of finding an accurate HST-model for a given data set, which remains robust to outliers, cannot only be demanding but also time consuming. The Bayesian model class selection approach imposes a penalisation against overly complex model candidates and admits a selection of the most plausible HST-model according to the maximum value of model evidence provided by the data or relative plausibility within a set of model class candidates. The potential of Bayes interference and its efficiency in an HST-model are presented on geodetic time series as a result of a permanent monitoring system on a rock-fill embankment dam. The method offers high potential for engineers in the decision making process, whilst the HST-model can be promptly adapted to new information given by new measurements and can enhance the safety and reliability of dams.

Suggested Citation

  • Gamse, Sonja & Zhou, Wan-Huan & Tan, Fang & Yuen, Ka-Veng & Oberguggenberger, Michael, 2018. "Hydrostatic-season-time model updating using Bayesian model class selection," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 40-50.
    • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:40-50
    DOI: 10.1016/j.ress.2017.07.018
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    References listed on IDEAS

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    1. M. Peng & L. Zhang, 2012. "Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(1), pages 903-933, October.
    2. Barone, Giorgio & Frangopol, Dan M., 2014. "Reliability, risk and lifetime distributions as performance indicators for life-cycle maintenance of deteriorating structures," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 21-37.
    3. Delenne, C. & Cappelaere, B. & Guinot, V., 2012. "Uncertainty analysis of river flooding and dam failure risks using local sensitivity computations," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 171-183.
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    3. Tang, Cong & He, Shu-Yu & Zhou, Wan-Huan, 2022. "Settlement-based framework for long-term serviceability assessment of immersed tunnels," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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