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Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks

  • Neural Networks in Art, sound and Design
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Abstract

Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits. However, the uncertainty and complexity of design parameters, such as the SiO2/Na2O mole ratio in alkaline solution, the alkaline solution concentration in liquid phase, and the liquid-to-fly ash mass ratio (L/F), have made it very difficult to create a systematic approach for geopolymer mix design. These mix design parameters, along with fly ash properties and curing conditions (temperature and time), significantly influence key properties of the material, such as setting time and compressive strength. In this study, an artificial neural network (ANN) was used to develop models for predicting the key properties of high-calcium fly ash-based geopolymer according to its mix design parameters. The correlations between experimental measurements and ANN model predictions of setting time, compressive strength, and heat of geopolymerization were established based on the results of tests on 36, 273, and 72 geopolymer mixes, respectively. The results show that the correlations between the experimental measurements and ANN model predictions of the properties studied are all strong. ANN modeling was found to be a suitable computing method to analyze the effects of design parameters on geopolymer properties and showed that L/F exhibited the greatest effect on setting time, alkaline solution concentration had the greatest influence on compressive strength, and a mole ratio larger than 1.5 significantly impacted heat at the geopolymerization peak. The developed ANN models can be used as guidance for mix design of high-calcium fly ash geopolymer in engineering applications.

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Acknowledgements

The present study is a part of the first author’s dissertation. He would like to acknowledge the support from the Institute for Transportation and the Department of Civil, Construction and Environmental Engineering at Iowa State University during his PhD study.

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Authors and Affiliations

  1. Department of Civil Construction and Environmental Engineering, Iowa State University, Ames, IA, 50011, USA

    Yifeng Ling & Kejin Wang

  2. School of Highway Engineering, Chang’an University, Xi’an, 710064, Shaanxi, China

    Xuhao Wang

  3. School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Wengui Li

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  1. Yifeng Ling
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  2. Kejin Wang
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  3. Xuhao Wang
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  4. Wengui Li
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Correspondence to Kejin Wang.

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Appendix

Appendix

This appendix includes Tables 8, 9, 10, 11, 12, and 13 listing the data sets for setting time, compressive strength, dissolution peak time, dissolution peak heat, geopolymerization peak time, and geopolymerization peak heat from the experiments used for ANN modeling.

Table 8 Data sets for setting time
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Table 9 Data sets for compressive strength
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Table 10 Data sets for dissolution peak time
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Table 11 Data sets for dissolution peak heat
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Table 12 Data sets for geopolymerization peak time
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Table 13 Data sets for geopolymerization peak heat
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Ling, Y., Wang, K., Wang, X. et al. Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks. Neural Comput & Applic 33, 85–105 (2021). https://doi.org/10.1007/s00521-019-04662-3

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  • DOI: https://doi.org/10.1007/s00521-019-04662-3

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