The surrogate model is applied to a reliability analysis of the stability of untrenched pipelines. Experience has shown that the stability of a pipeline is sensitive to a number of variables associated with soil properties, structural modelling, and hydrodynamic loads. Because uncertainties are unavoidable in these key design parameters, it is vital to evaluate pipeline instability failure using a probabilistic approach. However, a reliability analysis of on-bottom stability in the time domain is often a very computationally expensive process. The stability of a pipeline exposed to time-varying environmental loads is a highly nonlinear phenomenon with pipe–soil–fluid interactions. In this work, surrogate model technology was utilised to realise the reliability analysis which is also quite time consuming. A Fourier model was used to calculate the hydrodynamic forces acting on a pipeline. The surrogate model was constructed using experiment sampling and, in conjunction with a Monte Carlo simulation, was employed to perform the reliability evaluation. Moreover, the influences of various random variables on pipeline instability are discussed through a sensitivity analysis. An accurate assessment of the conservatism in the different variables could lead to fewer conservative stabilisation requirements. The results show that using a surrogate model for reliability analysis not only reduces the computational cost significantly but also produces a highly accurate evaluation of pipeline stability.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic stability analysis of pipeline based on reliability using surrogate model


    Contributors:
    Yang, Hezhen (author) / Wang, Aijun (author)

    Published in:

    Publication date :

    2013-04-01


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Surrogate model uncertainty quantification for active learning reliability analysis

    PANG, Yong / ZHANG, Shuai / LIANG, Pengwei et al. | Elsevier | 2024

    Free access

    A New Robust Surrogate Model: Reliability Based Hybrid Functions

    Zhang, Jie / Messac, Achille / Chowdhury, Souma et al. | AIAA | 2011


    A New Robust Surrogate Model: Reliability Based Hybrid Functions

    Zhang, J. / Chowdhury, S. / Messac, A. et al. | British Library Conference Proceedings | 2011


    An Equivalent Reliability Index Approach for Surrogate Model-based RBDO

    Li, Mingyang / Wang, Zequn / Wang, Pingfeng | AIAA | 2019


    Neural-Network Surrogate Model for Flow Stability Analysis Based on Parabolized Stability Equations

    Henderson, Trenton S. / Sanjaya, Devina P. / Olichevis Halila, Gustavo Luiz et al. | AIAA | 2025