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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.06294 |
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| _version_ | 1866917918137122816 |
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| author | Forster, David von Scheven, Malte |
| author_facet | Forster, David von Scheven, Malte |
| contents | The degree of static indeterminacy and its spatial distribution characterize load-bearing structures independent of a specific load case. The redundancy matrix stores the distribution of the static indeterminacy on its main diagonal, and thereby offers the possibility to use this property for the assessment of structures. It is especially suitable to be used in early planning stages for design exploration. In this paper, performance indicators with respect to robustness and assemblability are derived from the redundancy matrix. For each of the performance indicators, a detailed matrix-based derivation is given and the application is showcased with various truss examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Redundancy Matrix as a Performance Indicator for Structural Assessment Forster, David von Scheven, Malte Computational Engineering, Finance, and Science The degree of static indeterminacy and its spatial distribution characterize load-bearing structures independent of a specific load case. The redundancy matrix stores the distribution of the static indeterminacy on its main diagonal, and thereby offers the possibility to use this property for the assessment of structures. It is especially suitable to be used in early planning stages for design exploration. In this paper, performance indicators with respect to robustness and assemblability are derived from the redundancy matrix. For each of the performance indicators, a detailed matrix-based derivation is given and the application is showcased with various truss examples. |
| title | The Redundancy Matrix as a Performance Indicator for Structural Assessment |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2405.06294 |