Grammar-based generation of strut-and-tie models for designing reinforced concrete structures
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Date
2024-12-01Type
- Journal Article
ETH Bibliography
yes
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Abstract
Reinforced concrete structures featuring discontinuity regions are complex to design and often susceptible to errors linked to numerical analysis methods. For such structural design problems, strut-and-tie models offer a simple, intuitive and safe design method based on the lower bound theorem of plasticity. Although intuitive, the derivation of strut-and-tie models requires nonnegligible effort and a certain degree of expertise to navigate the highdimensional design space. The automated generation of strut-and-tie models is nontrivial with existing optimisation-based methods, which struggle with accounting for fabrication aspects or incorporating user adaptations.
This paper presents a novel grammar-based approach for generating practical strut-and-tie models by representing them as graphs and constructing a graph grammar. It consists of rules customised to consider engineering judgement, significantly reducing the dimensionality of the design space. The sequential application of such rules allows for human-computer interaction and aids engineers in decision-making, while being kept in the loop. Parsing four common design examples from the literature demonstrates the efficacy of this approach. The developed designs are more practical compared with existing optimisation-based suggestions. This interpretable grammar-based approach closely follows the intuitive decision-making process of practising structural engineers, which could be adapted to support further structural engineering design tasks. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000696289Publication status
publishedExternal links
Journal / series
Computers & StructuresVolume
Pages / Article No.
Publisher
ElsevierSubject
Reinforced concrete; Strut-and-tie models; Stress fields; Graph grammars; Generative design; Design explorationOrganisational unit
09469 - Kaufmann, Walter / Kaufmann, Walter
03890 - Chatzi, Eleni / Chatzi, Eleni
02219 - ETH AI Center / ETH AI Center
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ETH Bibliography
yes
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