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Proposing the combined MARS–PSO and ELM–PSO models for estimating the compressive strength of circular concrete columns wrapped with FRP sheets

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Abstract

The purpose of this study is to use multivariate adaptive regression spline (MARS) and extreme learning machine (ELM) artificial intelligence models to estimate the compressive strength of the circular concrete columns wrapped with FRP sheets. In addition, to improve the accuracy of these models, the particle swarm optimization (PSO) algorithm was used in combination with these models, and the accuracy of the models was evaluated to estimate the strength values. The results indicate that in general, the artificial intelligence models estimate the compressive strength of FRP-wrapped columns more accurately than the existing analytical models. In particular, the combined MARS–PSO model offered a better performance compared to the other models, as this model has correlation coefficients of 0.9972 and 0.9961 in the training and testing phases, respectively. Moreover, a combination of the PSO algorithm with the two models, MARS and ELM, improved their accuracy by 6.13 and 4.68%, respectively.

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Authors and Affiliations

  1. Semnan University, Semnan, Iran

    Mojtaba Hanteh

  2. K. N. Toosi University of Technology, Tehran, Iran

    Haniyeh Malek

  3. Civil Engineering Department, Semnan University, Semnan, Iran

    Ali Kheyroddin

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  1. Mojtaba Hanteh
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  2. Haniyeh Malek
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  3. Ali Kheyroddin
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Correspondence to Mojtaba Hanteh.

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Hanteh, M., Malek, H. & Kheyroddin, A. Proposing the combined MARS–PSO and ELM–PSO models for estimating the compressive strength of circular concrete columns wrapped with FRP sheets. Soft Comput 27, 15937–15953 (2023). https://doi.org/10.1007/s00500-023-08854-x

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