Lateral capacity of URM walls: a parametric study using macro and micro limit analysis predictions
This research investigates the texture influence of masonry walls’ lateral capacity by comparing analytical predictions performed via macro and micro limit analysis. In particular, the effect of regular and quasi-periodic bond types, namely Running, Flemish, and English, is investigated. A full fact...
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Veröffentlicht in: | Applied sciences 2022-11, Vol.12 (10834), p.10834 |
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Sprache: | eng |
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Zusammenfassung: | This research investigates the texture influence of masonry walls’ lateral capacity by comparing analytical predictions performed via macro and micro limit analysis. In particular, the effect of regular and quasi-periodic bond types, namely Running, Flemish, and English, is investigated. A full factorial dataset involving 81 combinations is generated by varying geometrical (panel and block aspect ratio, bond type) and mechanical (friction coefficient) parameters. Analysis of variance (ANOVA) approach is used to investigate one-way and two-way factor interactions for each parameter in order to assess how it affects the horizontal load multiplier. Macro and micro limit analysis predictions are compared, and the differences in terms of mass-proportional horizontal load multiplier and failure mechanism are critically discussed. Macro and micro limit analysis provide close results, demonstrating the reliability of such approaches. Furthermore, results underline how the panel and block aspect ratio had the most significant effect on both the mean values and scatter of results, while no significant effect could be attributed to the bond types.
This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit ISISE under reference UIDB/04029/2020. This study has been partly funded by the STAND4HERITAGE project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant agreement No. 833123), as an Advanced Grant. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app122110834 |