Machine-learning-enhanced variable-angle truss model to predict the shear capacity of RC elements with transverse reinforcement
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- @Article{DEDOMENICO:2023:prostr,
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author = "Dario {De Domenico} and Giuseppe Quaranta and
Qingcong Zeng and Giorgio Monti",
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title = "Machine-learning-enhanced variable-angle truss model
to predict the shear capacity of {RC} elements with
transverse reinforcement",
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journal = "Procedia Structural Integrity",
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volume = "44",
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pages = "1688--1695",
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year = "2023",
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note = "XIX ANIDIS Conference, Seismic Engineering in Italy",
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keywords = "genetic algorithms, genetic programming, Reinforced
concrete beams, Reinforced concrete columns, Design
code, Machine learning, Reinforced concrete, Shear
capacity, Variable-angle truss model, Eurocode",
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ISSN = "2452-3216",
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URL = "https://www.sciencedirect.com/science/article/pii/S245232162300224X",
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DOI = "doi:10.1016/j.prostr.2023.01.216",
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abstract = "This contribution presents a numerical model for the
shear capacity prediction of reinforced concrete (RC)
elements with transverse reinforcement. The proposed
model originates from one of the most popular
mechanical models adopted in building codes, namely the
variable-angle truss model. Starting from the
formulation proposed in the Eurocode 2, two empirical
coefficients governing the concrete contribution (i.e.,
the shear capacity ascribed to crushing of compressed
struts) are adjusted and enriched through machine
learning, in such a way to improve the predictive
efficiency of the model against experimental results.
More specifically, genetic programming is used to
derive closed-form expressions of the two corrective
coefficients, thus facilitating the use of this model
for practical purposes. The proposed expressions are
validated by comparison with a wide set of experimental
results collected from the literature concerning RC
beams and columns failing in shear under both monotonic
and cyclic loading conditions, respectively. It is
demonstrated that the proposed formulation, thanks to
the two novel corrective coefficients, not only attains
higher accuracy than the original Eurocode 2
formulation, but also outperforms many other existing
design code provisions while preserving a sound
mechanical basis",
- }
Genetic Programming entries for
Dario De Domenico
Giuseppe Quaranta
Qingcong Zeng
Giorgio Monti
Citations