Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)
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- @Article{Bin-Inqiad:2024:mtcomm,
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author = "Waleed {Bin Inqiad} and Muhammad Faisal Javed and
Muhammad Shahid Siddique and Naseer Muhammad Khan and
Loai Alkhattabi and Maher Abuhussain and
Hisham Alabduljabbar",
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title = "Comparison of boosting and genetic programming
techniques for prediction of tensile strain capacity of
Engineered Cementitious Composites ({ECC)}",
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journal = "Materials Today Communications",
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year = "2024",
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volume = "39",
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pages = "109222",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Engineered cementitious composites, Tensile
strain capacity, Fibres, Shapley additive analysis,
gene expression programming",
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ISSN = "2352-4928",
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URL = "
https://www.sciencedirect.com/science/article/pii/S2352492824012030",
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DOI = "
doi:10.1016/j.mtcomm.2024.109222",
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abstract = "Plain concrete is weak against tension and has low
Tensile Strain Capacity (TSC) which significantly
affects its long-term performance. To overcome this
issue, Engineered Cementitious Composites (ECC) were
developed by incorporating polymer fibres in the cement
matrix which increases ductility and provides higher
TSC than plain concrete and they have emerged as a
viable alternative to brittle plain concrete. This
study is conducted in an attempt to develop empirical
prediction models for TSC prediction of ECC without
requiring extensive experimental procedures. For this
purpose, two evolutionary programming techniques known
as Multi Expression Programming (MEP), Gene Expression
Programming (GEP) along with two boosting-based
techniques: AdaBoost and Extreme Gradient Boosting
(XGB) were developed using data collected from
published literature. The gathered dataset had seven
input parameters including water-to-binder ratio, sand,
fibre content, cement, fly ash, superplasticizer, and
age etc. and only one output parameter i.e., TSC. The
error assessment of developed models was done using
correlation coefficient, Mean Absolute Error (MAE), and
Objective Function (OF) etc. and the error comparison
showed that XGB has the highest accuracy having the
least OF value of 0.081 as compared to 0.11 of
AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley
additive analysis was conducted on the XGB model since
it proved to be the most accurate, and the results
highlighted that fibre content, age, and
water-to-binder ratio are the most important features
to predict TSC of ECC",
- }
Genetic Programming entries for
Waleed Bin Inqiad
Muhammad Faisal Javed
Muhammad Shahid Siddique
Naseer Muhammad Khan
Loai Alkhattabi
Maher Abuhussain
Hisham Alabduljabbar
Citations