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Reliability of Jack-up against Punch-through using Failure State Intelligent Recognition Technique

  • Tao Lyu
  • Changhang XuEmail author
  • Guoming Chen
  • Yipei Zhao
  • Qingyang Li
  • Tantan Zhao
Structural Engineering
  • 6 Downloads

Abstract

The preload operation of jack-up in complex multi-layered foundation requires enhanced understanding of its behaviour in punch-through accident and suitable safety analysis tools for the assessment of their reliability for a particular site. In this study, reliability analysis model of jack-up against punch-through is established considering structural uncertainty. In order to identify the failure state, an improved reliability solution method has been developed based on Sparse Auto-Encoder (SAE) deep learning network model. Sparse self-coding algorithm is used to the training of the deep network, and Softmax regression model is established to solve the identification and classification problem of the output layer. The first application of the technique was the study of HYSY 941 jack-up platform. More specifically, numerical calculations of structure ultimate bearing capacity have been undertaken, and the influence of model parameters on the prediction accuracy of the failure state is discussed. The results show that implicit performance function can be constructed accurately using SAE-MC method by reflecting the relationship between different critical safety state and structural vulnerability. Compared with traditional BP neural network, deep learning network has higher prediction accuracy to failure probability. The dynamic risk grade in the process of preload operation can be determined quantitatively using the reliability analysis method mentioned in this paper.

Keywords

jack-up punch-through reliability deep learning failure state identification 

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Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tao Lyu
    • 1
  • Changhang Xu
    • 1
    Email author
  • Guoming Chen
    • 1
  • Yipei Zhao
    • 1
  • Qingyang Li
    • 1
  • Tantan Zhao
    • 1
  1. 1.Centre for Offshore Engineering and Safety TechnologyChina University of PetroleumQingdaoChina

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