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Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine

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  • Shao-Fei Jiang
  • Da-Bao Fu
  • Si-Yao Wu

Abstract

To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.

Suggested Citation

  • Shao-Fei Jiang & Da-Bao Fu & Si-Yao Wu, 2014. "Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, January.
    • Handle: RePEc:hin:jnlmpe:586191
    DOI: 10.1155/2014/586191
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    Cited by:

    1. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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