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Neural Computing and Applications

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01.07.2013 | Original Article

New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach

verfasst von: Pejman Aminian, Hadi Niroomand, Amir Hossein Gandomi, Amir Hossein Alavi, Milad Arab Esmaeili

Erschienen in: Neural Computing and Applications | Ausgabe 1/2013

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Abstract

This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalization capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.

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Titel: New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach
verfasst von: Pejman Aminian
Hadi Niroomand
Amir Hossein Gandomi
Amir Hossein Alavi
Milad Arab Esmaeili
Publikationsdatum: 01.07.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1138-4

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