Machine-learning-aided improvement of mechanics-based code-conforming shear capacity equation for RC elements with stirrups
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- @Article{QUARANTA:2022:engstruct,
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author = "Giuseppe Quaranta and Dario {De Domenico} and
Giorgio Monti",
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title = "Machine-learning-aided improvement of mechanics-based
code-conforming shear capacity equation for {RC}
elements with stirrups",
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journal = "Engineering Structures",
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volume = "267",
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pages = "114665",
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year = "2022",
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ISSN = "0141-0296",
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DOI = "doi:10.1016/j.engstruct.2022.114665",
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URL = "https://www.sciencedirect.com/science/article/pii/S014102962200760X",
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keywords = "genetic algorithms, genetic programming, Beam, Column,
Design code, Machine learning, Reinforced concrete,
Shear capacity, Variable-angle truss model",
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abstract = "The development of shear capacity equations for
reinforced concrete (RC) beams and columns has been
historically pursued starting from the
conceptualization of a resisting mechanism. Recently,
machine learning techniques are attracting more and
more interest in this field. Mechanics-based and
data-driven approaches (i.e., white box and black box
modeling, respectively) have been considered
independently so far. Conversely, this work aims at
exploring a hybrid alternative way (i.e., gray box
modeling) for deriving the shear capacity equation for
RC beams and columns, in which a mechanics-based
code-conforming formulation is improved thanks to a
machine-learning-aided approach. Specifically, the
capacity equation currently in use within Europe that
relies on the variable-angle truss resisting mechanism
is enriched by means of Genetic Programming.
Easy-to-use novel expressions for the two fundamental
coefficients ruling the concrete contribution are
defined to better match experimental data. The
performance of the newly obtained equation is first
discussed within the largest comparative assessment
ever presented so far among shear strength formulations
reported into existing technical codes around the
world. Afterward, it is recast into a code-formatted
design capacity equation using a simple, yet reliable,
procedure. Overall, the results demonstrate that
merging mechanics-based and data-driven methods can be
beneficial in the development of capacity equations
since it allows preserving the physical meaning of the
resisting mechanism while enhancing the accuracy of the
final predictions by means of machine learning
techniques. Although the methodology is here applied to
evaluate the shear strength of RC beams and columns, it
is very general and can be readily extended to the
development of further capacity equations",
- }
Genetic Programming entries for
Giuseppe Quaranta
Dario De Domenico
Giorgio Monti
Citations