Predicting ultra-high-performance concrete compressive strength using gene expression programming method Article Swipe
Hisham Alabduljabbar
,
Majid Khan
,
Hamad Hassan Awan
,
Sayed M. Eldin
,
Rayed Alyousef
,
Abdeliazim Mustafa Mohamed
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cscm.2023.e02074
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cscm.2023.e02074
There have been extensive experimental studies available on the composition and characteristics of Ultra-High-Performance concrete (UHPC). However, the relation between UHPC characteristics and mixture content, on the other hand, is extremely non-linear and challenging to distinguish utilizing typical statistical approaches. A comprehensive literature research was carried out for this aim to acquire experimental data on the compressive strength of UHPC. The dataset contains 810 experimental values of compressive strength and 15 most influential parameters that include cement, water, nano-silica, quartz powder, limestone powder, gravel, sand, slag, superplasticizer, fiber, temperature, age, fly ash, relative humidity, and silica fume, are considered as input. The suggested gene expression programming (GEP) model can estimate the compressive strength of UHPC by using simple mathematical formulations. There is no predetermined function to evaluate in the GEP technique, and it replicates or eliminates numerous combinations of factors to create the formulation that suits the experimental results. For verification and validation of model performance, various statistical measures, SHAP analysis, external validation checks, and comparing with the regression model, are applied. SHAP analysis provided that age, fiber, silica fume, superplasticizer, cement, sand, and water have a high influence on compressive strength while other input parameters have less influence on compressive strength. The model outcomes indicate the robustness and accuracy of the predictive potential of the proposed model. As a result, the GEP model can be used to give practical insights into the mixture design of UHPC for a variety of construction applications, resulting in better predictive capacity at a cheaper cost and in a considerably shorter period. Also, the present study findings can assist the design engineers and builders to understand the significance of each constituent in UHPC.
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- Type
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- Language
- en
- Landing Page
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All OpenAlex metadata
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https://openalex.org/W4365512529Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.cscm.2023.e02074Digital Object Identifier
- Title
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Predicting ultra-high-performance concrete compressive strength using gene expression programming methodWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
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2023-04-14Full publication date if available
- Authors
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Hisham Alabduljabbar, Majid Khan, Hamad Hassan Awan, Sayed M. Eldin, Rayed Alyousef, Abdeliazim Mustafa MohamedList of authors in order
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https://doi.org/10.1016/j.cscm.2023.e02074Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.cscm.2023.e02074Direct OA link when available
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Compressive strength, Silica fume, Fly ash, Superplasticizer, Gene expression programming, Cement, Linear regression, Materials science, Computer science, Composite material, Machine learningTop concepts (fields/topics) attached by OpenAlex
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70Total citation count in OpenAlex
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2025: 28, 2024: 29, 2023: 13Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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