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A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

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Abstract

This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.

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Acknowledgments

The authors are thankful to Amir Hossein Alavi (Iran University of Science & Technology) for his support and stimulating discussions.

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Authors and Affiliations

  1. Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran

    Pejman Aminian & Milad Arab Esmaeili

  2. Tehran Municipality’s Beautification Organization, Tehran, Iran

    Mohamad Reza Javid

  3. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Abazar Asghari

  4. College of Civil Engineering, Tafresh University, Tafresh, Iran

    Amir Hossein Gandomi

Authors
  1. Pejman Aminian
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  2. Mohamad Reza Javid
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  3. Abazar Asghari
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  4. Amir Hossein Gandomi
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  5. Milad Arab Esmaeili
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Correspondence to Milad Arab Esmaeili.

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Aminian, P., Javid, M.R., Asghari, A. et al. A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method. Neural Comput & Applic 20, 1321–1332 (2011). https://doi.org/10.1007/s00521-011-0689-0

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  • DOI: https://doi.org/10.1007/s00521-011-0689-0

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