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Approach for the structural reliability analysis by the modified sensitivity model based on response surface function

Lin Zhu1, Jianchun Qiu1, Min Chen2

  • 1School of Mechanical Engineering, Yangzhou University, Yangzhou 225001, China.

Heliyon
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

A new sensitivity analysis model integrates Kriging and Sobol algorithms for improved structural reliability prediction. This enhanced method accurately assesses multiple condition parameters in large structures, identifying key influences like trolley position.

Keywords:
Kriging modelMultiple coupling parametersReliabilitySensitivityWorking conditions

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Area of Science:

  • Engineering
  • Computational Mechanics
  • Reliability Engineering

Background:

  • Classical sensitivity analysis models struggle with numerous parameters and lack prediction accuracy.
  • Understanding parameter impacts on structural reliability is crucial for engineering design.

Purpose of the Study:

  • To develop a revised sensitivity analysis model with enhanced prediction accuracy for structural reliability.
  • To quantitatively assess the influence of multiple coupling condition parameters on structural reliability.

Main Methods:

  • Integration of the Kriging model (response surface function) with the Sobol sensitivity algorithm.
  • Combination with experimental design, range verification, multi-body dynamics, and structural statics analysis.
  • Application to large structures with multiple influence parameters, validated on a port crane.

Main Results:

  • The revised model accurately predicts structural reliability, achieving an average accuracy of 95.91%.
  • Identified trolley position as the parameter with the biggest sensitivity influence and lifting speed as the least.
  • Demonstrated effectiveness for large structures with numerous coupling parameters.

Conclusions:

  • The proposed revised sensitivity model significantly improves the assessment of structural reliability.
  • It accurately quantifies the impact of multiple condition parameters, outperforming classical methods.
  • The model is effective for complex engineering systems like large port cranes.