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A novel feed-through Elman neural network for predicting the compressive and flexural strengths of eco-friendly jarosite mixed concrete: design, simulation and a comparative study

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Abstract

In order to estimate the compressive and flexural strengths of environmentally friendly jarosite mixed concrete, an effective prediction model based on the modified Elman neural network (also known as feed-through Elman neural network (FTENN), a recurrent neural network) tuned with back-propagation (BP) algorithm is developed. Jarosite is a toxic waste produced by the zinc industry that needs to be handled carefully before disposal. It is used as a partial replacement for cement in the production of concrete. The proposed FTENN model’s performance is also compared with that of the traditional Elman neural network (ENN), the diagonal recurrent neural network (DRNN), Jordan recurrent neural network (JRNN), and the well-known feed-forward neural network (FFNN) model. It is found that the proposed model has given better accuracy as compared to the other models since it has provided comparatively lower values of the root-mean-square error (RMSE), mean average error (MAE), and mean absolute percentage error (MAPE) during the training and testing stage.

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Funding

The author TG has contributed to the analysis, design, material, and data-set preparation and the author RK has contributed to the model’s design, formulation, analysis, and simulations. The whole draft of the manuscript was written jointly by TG and RK.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Tanvi Gupta

  2. Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Rajesh Kumar

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  1. Tanvi Gupta
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  2. Rajesh Kumar
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The authors, TG and RK, have contributed equally to the study, conception, design, material preparation, and analysis. The whole draft of the manuscript was written jointly by TG and RK.

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Correspondence to Rajesh Kumar.

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Gupta, T., Kumar, R. A novel feed-through Elman neural network for predicting the compressive and flexural strengths of eco-friendly jarosite mixed concrete: design, simulation and a comparative study. Soft Comput 28, 399–414 (2024). https://doi.org/10.1007/s00500-023-08195-9

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