Predicting the Strength of CFRP-steel joints using Genetic Programming

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@Article{Pathan:2018:IOP,

• author = "Mohiuddin Pathan and Alaa Al-Mosawe and Riadh Al-Mahaidi",
• title = "Predicting the Strength of {CFRP}-steel joints using Genetic Programming",
• journal = "IOP Conference Series: Materials Science and Engineering",
• year = "2018",
• volume = "433",
• number = "1",
• pages = "012028",
• month = nov,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1088/1757-899X/433/1/012028",
• size = "9 pages",
• abstract = "Numerous steel structures that were built following the industrial revolution, including bridges, off-shore platforms, and many buildings, are carrying excess loads of varying types over those they were originally designed for. Furthermore, the magnitude, pattern, and type of loadings have changed over the years. As a result, these structures need to be strengthened to sustain and convey the increased applied loads and remain in service. Carbon fibre reinforced polymers are a promising material that is gaining popularity in the field of strengthening deteriorated infrastructure as a replacement for conventional strengthening methods such as bolting, riveting, or welding due to its cost effectiveness, good strength-to-weight ratio, and ease of application. This paper proposes a new model to predict the strength of CFRP-steel joints using genetic programming. A number of studies have been carried out to evaluate the bond strength of newly formed composite material, but a lack of calculations for the bond strength with assurance still exists. A prediction model derived using genetic programming to calculate bond strength for both static and dynamic loading scenarios using various bond length, cross-sectional area, and CFRP moduli is thus proposed. The database used in the genetic program software was collated from the existing literature, and both derived models have a high value of R-squared which demonstrates an acceptable level of accuracy compared to the experimented results.",
• notes = "IOP poor web site",

}

Genetic Programming entries for Mohiuddin Pathan Alaa Al-Mosawe Riadh Al-Mahaidi

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