Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams
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- @Article{SOLHMIRZAEI:2020:ES,
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author = "Roya Solhmirzaei and Hadi Salehi and
Venkatesh Kodur and M. Z. Naser",
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title = "Machine learning framework for predicting failure mode
and shear capacity of ultra high performance concrete
beams",
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journal = "Engineering Structures",
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volume = "224",
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pages = "111221",
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year = "2020",
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ISSN = "0141-0296",
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DOI = "doi:10.1016/j.engstruct.2020.111221",
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URL = "https://www.sciencedirect.com/science/article/pii/S0141029620338220",
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keywords = "genetic algorithms, genetic programming, Ultra high
performance concrete (UHPC), Machine learning, Failure
mode, Shear capacity, Artificial intelligence,
Data-driven framework",
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abstract = "This paper presents a data-driven machine learning
(ML) framework for predicting failure mode and shear
capacity of Ultra High Performance Concrete (UHPC)
beams. To this end, a comprehensive database on 360
reported tests on UHPC beams with different geometric,
fiber properties, loading and material characteristics
was collected. This database was then analyzed using
different ML algorithms including, support vector
machine (SVM), artificial neural networks (ANN),
k-nearest neighbor (k-NN), and genetic programing (GP),
to identify key parameters governing failure pattern
and shear capacity of UHPC beams. The outcome of this
analysis is a computational-based ML framework that is
capable of identifying failure mode of UHPC beams and
simplified expressions for predicting shear capacity of
UHPC beams. Predictions obtained from the proposed
framework was compared against the values obtained from
design equations in codes, and also results from
full-scale tests to show the reliability of the
proposed approach. The results clearly infer that the
proposed data-driven ML framework can effectively
predict failure mode and shear capacity of prestressed
and non-prestressed UHPC beams with varying
reinforcement detailing and configurations",
- }
Genetic Programming entries for
Roya Solhmirzaei
Hadi Salehi
Venkatesh Kodur
M Z Naser
Citations