Benchmark Numerical Model for Progressive Collapse Analysis of RC Beam-Column Sub-Assemblages
Autor(en): |
Bilal El-Ariss
Said Elkholy Ahmed Shehada |
---|---|
Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 18 Januar 2022, n. 2, v. 12 |
Seite(n): | 122 |
DOI: | 10.3390/buildings12020122 |
Abstrakt: |
The pertinence of the fiber element approach to enable thorough numerical investigation on the potential for progressive collapse of reinforced concrete (RC) frame structures owing to interior column exclusion is examined using twenty-nine RC sub-assemblages with five different test setups and three different test scales. A qualitative examination of the results reveals a good agreement between the test results and the outcomes of the fiber-element-based numerical model using the finite element package SeismoStruct. Moreover, minor discrepancies between the test and numerical data demonstrate the capability of the fiber-element-based model to accurately simulate the behavior of RC elements with various boundary conditions and scales under the context of progressive collapse. Given the costly nature of experimental research, test errors, and the lengthy testing process, the proposed numerical model based on the fiber element approach can be considered a viable option for analyzing structures under progressive collapse due to the interior column exclusion scenario. Engineers and researchers can use the conclusions and comments highlighted in the study as a guide to create accurate models for the analysis of RC structures subjected to progressive collapse. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
6.62 MB
- Über diese
Datenseite - Reference-ID
10657761 - Veröffentlicht am:
17.02.2022 - Geändert am:
01.06.2022