The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{BAGHERI:2019:Measurement,

• author = "Ali Bagheri and Ali Nazari and Jay Sanjayan",
• title = "The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by {ANN} and {GP}",
• journal = "Measurement",
• volume = "141",
• pages = "241--249",
• year = "2019",
• ISSN = "0263-2241",
• DOI = "doi:10.1016/j.measurement.2019.03.001",
• URL = "http://www.sciencedirect.com/science/article/pii/S0263224119302106",
• keywords = "genetic algorithms, genetic programming, Boron-activated geopolymer, Artificial intelligence, Aluminosilicate, Machine learning, Energy and resources",
• abstract = "This paper employs artificial intelligence methods in order to create a function for compressive strength of the boroaluminosilicate geopolymers based on mixture proportion variables. Boroaluminosilicate geopolymers (BASGs), a group of boron-based alkali-activated materials, not only minimise the carbon footprint in the construction industry but also decrease the consumption of energy and natural resources. Australian fly ash and iron making slag are activated in sodium and boron-based alkaline medium in order to produce the geopolymer binders. The current study employs artificial neural network in order to classify the collected data into train, test, and validation followed by genetic programming for developing a function to approximate the compressive strength of BASGs. The independent variables comprise the percentage of fly ash and slag as well as ratios of boron, silicon, and sodium ions in the alkaline solution. The performance of each method is assessed by the acquired regression and the error parameters. The obtained results show that the percent of silicon and boron ions, with positive direct correlation and the largest power in the function respectively, have the most significant effects on the compressive strength of BASG. The assessment factors, including R-squared 0.95 and root-mean-square error 0.07 in the testing data, indicate that the model explains all the variability of the response data around its mean. It implies a high level of accuracy and reliability for the model",

}

Genetic Programming entries for Ali Bagheri Ali Nazari Jay G Sanjayan

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