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,

• 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",
• title = "Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites ({ECC)}",
• journal = "Materials Today Communications",
• year = "2024",
• volume = "39",
• pages = "109222",
• keywords = "genetic algorithms, genetic programming, Machine learning, Engineered cementitious composites, Tensile strain capacity, Fibres, Shapley additive analysis, gene expression programming",
• ISSN = "2352-4928",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352492824012030",
• DOI = "doi:10.1016/j.mtcomm.2024.109222",
• 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