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Modelling and multi objective optimization of bamboo reinforced concrete beams using ANN and genetic algorithms

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Abstract

In this research, the costs as well as flexural and tensile strength of bamboo reinforced concrete material were predicted and optimized using artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II). The inputs to the ANN were curing days and percentage bamboo content in the bamboo reinforced concrete material, while the outputs were cost, flexural and tensile strength. The ANN predicted the experimentally determined values of the tensile strength, flexural strength and costs of the bamboo reinforced concrete material excellently with correlation coefficients of 0.99635, 0.99739 and 1, respectively. Subsequently, the ANN was used as the fitness function for NSGA-II for multi-objective optimization of the cost, flexural and tensile strength of bamboo reinforced concrete material. The Pareto optimal solution obtained could serve as a design guide for engineers for optimal design of structures using cost, flexural and tensile strength of bamboo reinforced concrete material.

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Authors and Affiliations

  1. Department of Civil Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria

    Ikechukwu Etienne Umeonyiagu

  2. Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria

    Chidozie Chukwuemeka Nwobi-Okoye

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  1. Ikechukwu Etienne Umeonyiagu
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  2. Chidozie Chukwuemeka Nwobi-Okoye
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Correspondence to Chidozie Chukwuemeka Nwobi-Okoye.

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Umeonyiagu, I.E., Nwobi-Okoye, C.C. Modelling and multi objective optimization of bamboo reinforced concrete beams using ANN and genetic algorithms. Eur. J. Wood Prod. 77, 931–947 (2019). https://doi.org/10.1007/s00107-019-01418-7

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  • DOI: https://doi.org/10.1007/s00107-019-01418-7

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