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A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method

Author

Listed:
  • Yuan Chen
  • Zhijie Zhou
  • Lihao Yang
  • Guanyu Hu
  • Xiaoxia Han
  • Shuaiwen Tang

Abstract

The structural safety assessment of large liquid tanks (LLT) has attracted an extensive attention. As a typical gray box model, the belief rule base (BRB) model can handle qualitative information and quantitative data simultaneously, which is a suitable modeling tool for structural safety assessment. However, it is difficult to establish and train the BRB model when there is a lack of expert experience and fault samples of LLT. Therefore, a novel safety assessment model for LLT based on BRB and finite element method (FEM-BRB) is proposed in this paper. The FEM is introduced to construct the BRB model by combining expert knowledge and industry standards for the first time, which can effectively compensate for the lack of expert experience. The fault samples are generated in the mechanism simulation model under different working conditions. Based on the fault samples generated by the FEM and historical samples, the projection covariance matrix adaption evolution strategy (P-CMA-ES) optimization algorithm is then used to train the model, which further improves the structural safety assessment accuracy when lacking fault samples. A case study of three actual oil tanks in a coastal port is conducted to illustrate the effectiveness and advantage of the developed structural safety assessment method.

Suggested Citation

  • Yuan Chen & Zhijie Zhou & Lihao Yang & Guanyu Hu & Xiaoxia Han & Shuaiwen Tang, 2022. "A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method," Journal of Risk and Reliability, , vol. 236(3), pages 458-476, June.
  • Handle: RePEc:sae:risrel:v:236:y:2022:i:3:p:458-476
    DOI: 10.1177/1748006X211021690
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    References listed on IDEAS

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    1. Bernier, Carl & Padgett, Jamie E., 2019. "Fragility and risk assessment of aboveground storage tanks subjected to concurrent surge, wave, and wind loads," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Vema, Vamsikrishna & Sudheer, K.P. & Chaubey, I., 2019. "Fuzzy inference system for site suitability evaluation of water harvesting structures in rainfed regions," Agricultural Water Management, Elsevier, vol. 218(C), pages 82-93.
    3. Yang, Yunfeng & Chen, Guohua & Reniers, Genserik, 2020. "Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Feng, Zhichao & Zhou, Zhijie & Hu, Changhua & Ban, Xiaojun & Hu, Guanyu, 2020. "A safety assessment model based on belief rule base with new optimization method," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    6. Olivar, Oscar J. Ramírez & Mayorga, Santiago Zuluaga & Giraldo, Felipe Muñoz & Sánchez-Silva, Mauricio & Pinelli, Jean-Paul & Salzano, Ernesto, 2020. "The effects of extreme winds on atmospheric storage tanks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Kong, Guilan & Xu, Dong-Ling & Body, Richard & Yang, Jian-Bo & Mackway-Jones, Kevin & Carley, Simon, 2012. "A belief rule-based decision support system for clinical risk assessment of cardiac chest pain," European Journal of Operational Research, Elsevier, vol. 219(3), pages 564-573.
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