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Artificial Neural Network Combined with Grey Wolf Optimizer for Period Determination of Light-Frame Wood Buildings

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Proceedings of the 7th International Conference on Architecture, Materials and Construction (ICAMC 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 226))

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Abstract

The paper investigates the accuracy and competitiveness of the Grey Wolf Optimizer (GWO) algorithm employing the Feed Forward (FF) neural network. The algorithm is implemented on the period database of 47 light-frame wood buildings samples of different dimensions and geometry. Five physical characteristics were selected as input variables to determine the fundamental period of light-frame wood buildings. The structure of the Artificial Neural Network (ANN) was optimized using the GWO algorithm. The model’s accuracy was evaluated by comparing the results with the Multiple Linear Regression (MLR) model and the model available in the National Building Code of Canada (NBCC, 2015). The results indicated the accuracy of the GWO algorithm optimized in ANN over other models for predicting the period of light-frame wood buildings.

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Authors and Affiliations

  1. Department of Building, Civil and Environmental Engineering, Concordia University, Montréal, Canada

    M. Nikoo & G. Hafeez

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  1. M. Nikoo
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  2. G. Hafeez
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Correspondence to G. Hafeez .

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Editors and Affiliations

  1. University of Minho, Guimarães, Portugal

    Paulo Mendonça

  2. Faculdade de Arquitetura, University of Lisbon, Lisbon, Portugal

    Nuno Dinis Cortiços

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Nikoo, M., Hafeez, G. (2022). Artificial Neural Network Combined with Grey Wolf Optimizer for Period Determination of Light-Frame Wood Buildings. In: Mendonça, P., Cortiços, N.D. (eds) Proceedings of the 7th International Conference on Architecture, Materials and Construction. ICAMC 2021. Lecture Notes in Civil Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-94514-5_22

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  • DOI: https://doi.org/10.1007/978-3-030-94514-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94513-8

  • Online ISBN: 978-3-030-94514-5

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