Comparison Between Phenomenological and Fiber-Section Non-linear Models
| Autor: | Guido Camata, Enrico Spacone, Marco Terrenzi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Geography
Planning and Development 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Plasticity predictive equations 0201 civil engineering Structural element lcsh:HT165.5-169.9 Simple (abstract algebra) Phenomenological model plastic hinge moment of yielding Mathematics 021110 strategic defence & security studies Fiber (mathematics) Mathematical analysis modeling Building and Construction lcsh:City planning Urban Studies phenomenological model lcsh:TA1-2040 Section (archaeology) Plastic hinge Element (category theory) lcsh:Engineering (General). Civil engineering (General) fiber |
| Zdroj: | Frontiers in Built Environment, Vol 6 (2020) |
| ISSN: | 2297-3362 |
| DOI: | 10.3389/fbuil.2020.00038 |
| Popis: | The prediction of the seismic response of structures requires accurate models for describing the behavior of each structural element. In frame analysis, non-linearities are typically modeled through either lumped or distributed plasticity elements. Another important assumption is needed for the selection of the appropriate section model. Phenomenological laws for the section behavior are computationally faster but less accurate than fiber-section models. This study compares the predictions obtained for reinforced concrete sections and simple single columns with a phenomenological section law (combined with a lumped plasticity element) and with a fiber-section model (combined with a distributed plasticity element). The phenomenological section model is that proposed by Ibarra et al. (2005) with the predictive equations used by Haselton et al. (2008). The fiber section distributed element is that by Spacone et al. (1996a,b). Comparisons show some important differences in the section responses (particularly for high levels of axial load) and in predicting the responses of experimentally tested columns. |
| Databáze: | OpenAIRE |
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