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Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles

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Abstract

The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is an essential mechanical indicator for judging the quality of concrete. Traditional test methods for determining the CS of GPC are expensive, time-consuming and limiting due to the complicated interplay of a wide variety of mixing proportions and curing regimes. Therefore, in this study, artificial neural network (ANN), multi-expression programming, full quadratic, linear regression and M5P-tree machine learning techniques were used to predict the CS of GPC. In this instance, around 207 tested CS values were extracted from the literature and studied to promote the models. During the process of modeling, eleven effective variables were utilized as input model parameters, and one variable was utilized as an output. Four statistical indicators were used to judge how well the models worked, and the sensitivity analysis was carried out. According to the results, the ANN model calculated the CS of GPC with greater precision than the other models. On the other hand, the ratio of alkaline solution to the binder, molarity, NaOH content, curing temperature and concrete age have substantial effects on the CS of GPC.

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Authors and Affiliations

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

    Hemn Unis Ahmed, Ahmed S. Mohammed, Aso A. Abdalla & Azad A. Mohammed

  2. Civil Engineering Department, Komar University of Science and Technology, Sulaimani, Kurdistan Region, Iraq

    Hemn Unis Ahmed

  3. Civil Engineering Department, University of Halabja, Halabja, Kurdistan Region, Iraq

    Rabar H. Faraj

  4. Civil Engineering Department, College of Engineering, University of Duhok, Duhok, Kurdistan Region, Iraq

    Shaker M. A. Qaidi

  5. Civil Engineering Department, University of Garmian, Kalar, Kurdistan Region, Iraq

    Nadhim Hamah Sor

  6. Department of Civil Engineering, College of Engineering, Nawroz University, 42001, Duhok, Iraq

    Shaker M. A. Qaidi

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Contributions

HUA was responsible for conceptualization and investigation; HUA, AAM, RHF, ASM., AAA, SMAQ and NHS were involved in methodology, software, data curation, writing—reviewing and editing, writing—original draft preparation and visualization; AAM, RHF, ASM, AAA, SMAQ and NHS participated in validation; HUA, AAM and ASM took part in formal analysis and project administration; AAA contributed to resources; and AAM and ASM were responsible for supervision. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hemn Unis Ahmed.

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Ahmed, H.U., Mohammed, A.S., Faraj, R.H. et al. Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles. Neural Comput & Applic 35, 12453–12479 (2023). https://doi.org/10.1007/s00521-023-08378-3

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