JOURNAL ARTICLE

Damage Assessment of Multi-Box Structures Subjected to Internal Blast Loadings.

  • Published In: International Journal of Structural Stability & Dynamics, 2024, v. 24, n. 7. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Lumeng; Zhang, Duo; Yao, Shujian; Cai, Zhenqing; Gan, Xiaowei; Huang, Shiqi 3 of 3

Abstract

In this study, a rapid algorithm is proposed to efficiently predict the damage of target structures subjected to internal explosions. Firstly, a data-driven approach was used to obtain the quasi-static peak pressure of the blast-loaded chamber based on a large amount of experimental data. The variation of the gas mass in each chamber was obtained using a pressure relief algorithm. The SDOF method was used to calculate the deformation of the wall plates to further calculate the change in volume and open area of the chamber. The change in pressure was calculated using the ideal gas equation of state. The proposed algorithm was then used to calculate the pressure change and wall deformation in each box. The results were compared with existing experimental data and found to be in good agreement. Finally, the results of the proposed algorithm are visualized, and the effects of different influencing factors such as explosion equivalent, cavity size, and wall thickness on the failure characteristics of multi-box structures were studied. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Structural Stability & Dynamics. 2024/04, Vol. 24, Issue 7, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0219-4554
  • DOI:10.1142/S0219455424500743
  • Accession Number:176341851
  • Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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