Development of prediction models for shear strength of SFRCB using a machine learning approach

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@Article{Sarveghadi:2016:NCA,

• author = "Masoud Sarveghadi and Amir H. Gandomi and Hamed Bolandi and Amir H. Alavi",
• title = "Development of prediction models for shear strength of {SFRCB} using a machine learning approach",
• journal = "Neural Computing and Applications",
• year = "2019",
• volume = "31",
• number = "7",
• pages = "2085--2094",
• month = jul,
• keywords = "genetic algorithms, genetic programming, SFRCB, Multi-expression programming, Shear strength, Prediction",
• ISSN = "0941-0643",
• DOI = "doi:10.1007/s00521-015-1997-6",
• size = "10 pages",
• abstract = "In this study, new design equations were derived for the assessment of shear resistance of steel fibre-reinforced concrete beams (SFRCB) using multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modelling of mechanical behaviour without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.",

}

Genetic Programming entries for Masoud Sarveghadi A H Gandomi Hamed Bolandi A H Alavi

Citations