Utilization of magnetic water in cementitious adhesive for near-surface mounted CFRP strengthening system

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{ALSAFY:2019:CBM,

• author = "Rawaa Al-Safy and Alaa Al-Mosawe and Riadh Al-Mahaidi",
• title = "Utilization of magnetic water in cementitious adhesive for near-surface mounted {CFRP} strengthening system",
• journal = "Construction and Building Materials",
• volume = "197",
• pages = "474--488",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Magnetic water, Cement-based adhesive, NSM, CFRP, Concrete, GP modelling",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2018.11.219",
• URL = "http://www.sciencedirect.com/science/article/pii/S0950061818329143",
• abstract = "Cement-based adhesive (CBA) is used as a bonding agent in Carbon Fibre Reinforced Polymer (CFRP) applications as an alternative to epoxy-based adhesive due to the drawbacks of the epoxy system under severe service conditions which negatively affect the bond between the CFRP and strengthened elements. This paper reports the results of, an investigation carried out to develop two types of CBA using magnetized water (MW) for mixing and curing. Two magnetic devices (MD-I and MD-II), with different magnetic field strengths (9000 and 6000 Gauss) respectively, were employed for water magnetization. Different water flows with different water circulation times in the magnetizer were used for each device. Compressive and splitting tensile strength tests of the magnetized CBA (MCBA) were conducted for different curing periods (3. 7, 14, 21 and 28a days) using MW. It was found that MW treatment increases the strength of CBA. The highest strength was obtained for MCBA samples when MD-I was used at a low flow rate (Fa =a 0.1a m3/hr) for 15 mins of circulation time (T). The latter was found to positively affect MCBA properties when T was increased from 15a min to 60a mins. Prediction of the compressive and tensile strength values are also studied in this paper using genetic programming, the models showed good correlation with the experimental data",

}

Genetic Programming entries for Rawaa Al-Safy Alaa Al-Mosawe Riadh Al-Mahaidi

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