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Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete

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Abstract

Geopolymer concrete is an eco-efficient and environmentally friendly construction material. Various ashes were used as the binder in geopolymer concrete, such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash. Fly ash was commonly consumed to prepare geopolymer concrete composites. It is essential to have 28 days resting period of the concrete to attain compressive strength in the structural design. In the present investigation, several soft computing models were employed to form the predictive models for forecasting the compressive strength of ground granulated blast furnace slag (GGBFS) concrete. A complete dataset of 268 samples was extracted from published research articles and analyzed to establish models. The modeling process incorporated seven effective parameters such as water content (W), temperature (T), water-to-binder ratio (w/b), ground granulated blast furnace slag-to-binder ratio (GGBFS/b), fine aggregate (FA) content, coarse aggregate (CA) content, and the superplasticizer dosage (SP) that were examined and measured on the compressive strength of GGBFS concrete by utilizing various modeling techniques, viz., Linear Regression (LR), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Regression (SVR), Grey Wolf Optimization (GWO), Differential Evolution (DE), and Mantra Rays Foraging Optimization (MRFO). The compressive strength of the training datasets was predicted using the SVR-PSO and SVR-GWO models, with a reliable coefficient of correlation of 0.9765 and 0.9522, respectively.

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Data availability

Data used in this research are available upon request.

Code availability

The code used in this research is available upon request.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. College of Engineering, Department of Civil Engineering, University of Sulaimani, Kurdistan Region, 46001, Sulaimaniyah, Iraq

    Hemn Unis Ahmed, Ahmed Mohammed & Azad Qadir

  2. Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt

    Reham R. Mostafa

  3. Civil Engineering Department, Chandigarh university, Mohali, Punjab, India

    Parveen Sihag

Authors
  1. Hemn Unis Ahmed
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  2. Reham R. Mostafa
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  3. Ahmed Mohammed
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  4. Parveen Sihag
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  5. Azad Qadir
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Contributions

HUA involved in conceptualization, methodology, validation, formal analysis, writing—original draft. RRM took part in methodology, writing—review & editing. AM participated in methodology, writing—review & editing. AQ took part in methodology, writing—review & editing.

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Correspondence to Hemn Unis Ahmed or Ahmed Mohammed.

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Ahmed, H.U., Mostafa, R.R., Mohammed, A. et al. Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete. Neural Comput & Applic 35, 2909–2926 (2023). https://doi.org/10.1007/s00521-022-07724-1

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