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Exploring the potential of neural network in assessing mechanical properties of geopolymer concrete incorporating fly ash and phosphogypsum in pavement applications

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Abstract

The purpose of this research study is twofold: (1) to investigate the factors that influence the mechanical properties of Geopolymer Concrete (GPC) made from fly ash (FA) and phosphogypsum (PG), and (2) to develop a method for predicting the mechanical properties based on experimental data. To achieve this, the researchers prepared GPC samples with varying levels of PG powder as a replacement for FA at different sodium hydroxide (NaOH) molarities, and tested their mechanical properties. The optimum mix design was obtained at 30% partial replacement of FA with PG at 12 M NaOH, and maximum compressive strength achieved was 47.97 MPa. Three distinct artificial neural network (ANN) techniques were then employed to predict the mechanical properties (Compressive Strength, Flexural Strength, and Split Tensile Strength) of the GPC using the experimental data. The accuracy of the predictions was assessed using performance statistics based on coefficient of determination (R2), root mean square error (RMSE), and Taylor diagram. The ANN models show good prediction efficiency with R2 value above 0.85.

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Data availability

All the data used for the development of model has already been included in the manuscript. Other data related to this study may be made available on reasonable request from the corresponding author.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India

    Bheem Pratap, Kumar Shubham & Somenath Mondal

  2. School of Infrastructure, Indian Institute of Technology, Bhubaneswar, 752050, Odisha, India

    Bendadi Hanumantha Rao

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  1. Bheem Pratap
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  2. Kumar Shubham
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  3. Somenath Mondal
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  4. Bendadi Hanumantha Rao
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Contributions

BP: Methodology, Investigation, Writing—original draft. KS: Conceptualization, Methodology and editing. SM: Writing – review, editing & Supervision. BHR: Conceptualization, Methodology, editing & Supervision.

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Correspondence to Bheem Pratap.

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Pratap, B., Shubham, K., Mondal, S. et al. Exploring the potential of neural network in assessing mechanical properties of geopolymer concrete incorporating fly ash and phosphogypsum in pavement applications. Asian J Civ Eng 24, 3575–3589 (2023). https://doi.org/10.1007/s42107-023-00735-w

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