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Developing an ANN prediction model for compressive strength of fly ash-based geopolymer concrete with experimental investigation

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Abstract

In recent times, the research on geopolymer concrete (GPC) using fly ash was performed extensively, due to its comparable properties as similar to cement and its environmental benefits. However, due to the complexity and uncertainty of the design parameters like molarity, alkaline solution concentration and liquid to fly ash (L/F) ratio had made it hard to develop a systematic mix design for geopolymer concrete. These parameters along with the properties of fly ash, curing temperature and curing time have a significant effect on compressive strength of geopolymer concrete. This paper describes the use of artificial neural network (ANN) to predict the compressive strength of the geopolymer concrete using the data obtained experimentally. The ANN is modelled using the MATLAB and thus used to predict the compressive strength using the specified input parameter. The outcomes of this research shed light on influence of curing temperature and time on compressive strength of geopolymer concrete. The developed ANN model demonstrated to be efficient tool for predicting the compressive strength with \({R}^{2}\) value of 0.85.

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Data availability

Data will be made available on reasonable request.

Abbreviations

ANN:

Artificial neural network

CA:

Coarse aggregate

CC:

Conventional concrete

CS:

Compressive strength

FA:

Fine aggregate

GPC:

Geopolymer concrete

ML:

Machine learning

MSE:

Mean square error

NTPC:

National thermal power corporation limited

OPC:

Ordinary Portland cement

RMSE:

Root-mean-square error

SCM:

Supplementary cementitious materials

XRF:

X-ray fluorescence

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Acknowledgements

The authors express thanks to the members of Civil Engineering Department of Guru Ghasidas Vishwavidyalaya, Bilaspur (C.G.), India, for their constant support in the completion of this research.

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  1. Civil Engineering Department, Guru Ghasidas Vishwavidyalaya, Bilaspur, 495009, India

    Nikhil Kumar Verma, Chakradhara Rao Meesala & Shailendra Kumar

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  1. Nikhil Kumar Verma
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Correspondence to Nikhil Kumar Verma.

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Verma, N.K., Meesala, C.R. & Kumar, S. Developing an ANN prediction model for compressive strength of fly ash-based geopolymer concrete with experimental investigation. Neural Comput & Applic 35, 10329–10345 (2023). https://doi.org/10.1007/s00521-023-08237-1

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