Advancing Structural Safety: Genetic Programming Approaches to Steel Fiber-Reinforced Concrete (SFRC) Blast Response Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Ali:2025:ICCAE,
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author = "Mohsin Ali and Maher Ali Rusho and Li Chen3 and
Dany Marcelo Tasan Cruz",
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title = "Advancing Structural Safety: Genetic Programming
Approaches to Steel Fiber-Reinforced Concrete ({SFRC)}
Blast Response Prediction",
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booktitle = "2025 17th International Conference on Computer and
Automation Engineering (ICCAE)",
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year = "2025",
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pages = "183--187",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Training,
Accuracy, Loading, Machine learning, Predictive models,
Mathematical models, Concrete, Steel, Load modelling,
Steel Fiber Reinforced Concrete (SFRC), Blast loading,
gene expression programming",
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ISSN = "2154-4360",
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DOI = "
doi:10.1109/ICCAE64891.2025.10980530",
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abstract = "Steel Fiber-Reinforced Concrete (SFRC) has emerged as
a preferred material for blast-resistant structures due
to its exceptional mechanical properties and energy
absorption capabilities. This study introduces a
machine learning-based framework to predict the maximum
displacement of SFRC structural members under blast
loading. Using 107 experimental data points, split into
$\mathbf{7 0 \percent}$ for training and 15 percent
each for validation and testing, Gene Expression
Programming (GEP) and Multi-Expression Programming
(MEP) were applied. The GEP model exhibited superior
predictive performance with R -values of 0.964
(training), 0.968 (validation), and 0.960 (testing),
while the MEP model achieved reasonable accuracy with
$R$-values of 0.922, 0.905, and 0.948, respectively.
Additionally, parametric analysis revealed the
influence of fiber properties on SFRC behaviour. This
approach not only simplifies predictive modelling but
also enhances its reliability, offering valuable
insights for optimising SFRC design under extreme
conditions and contributing to the advancement of
resilient structural systems.",
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notes = "Also known as \cite{10980530}
School of Civil Engineering, Southeast University,
Nanjing, China",
- }
Genetic Programming entries for
Mohsin Ali
Maher Ali Rusho
Li Chen3
Dany Marcelo Tasan Cruz
Citations