Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{onyelowe:2022:Designs,

• author = "Kennedy C. Onyelowe and Jagan Jayabalan and Ahmed M. Ebid and Pijush Samui and Rahul Pratap Singh and Atefeh Soleymani and Hashem Jahangir",
• title = "Evaluation of the Compressive Strength of {CFRP-Wrapped} Circular Concrete Columns Using Artificial Intelligence Techniques",
• journal = "Designs",
• year = "2022",
• volume = "6",
• number = "6",
• pages = "Article No. 112",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2411-9660",
• URL = "https://www.mdpi.com/2411-9660/6/6/112",
• DOI = "doi:10.3390/designs6060112",
• abstract = "The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8percent and a coefficient of determination R2 of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations.",
• notes = "also known as \cite{designs6060112}",

}

Genetic Programming entries for Kennedy C Onyelowe Jagan Jayabalan Ahmed M Ebid Pijush Samui Rahul Pratap Singh Atefeh Soleymani Hashem Jahangir

Citations