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Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN

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Abstract

In the present research, the information on compressive strength of self-compacting concrete (SCC) containing rice husk ash (RHA) and calcium carbide waste (CCW) as an admixture cured for 28 days was provided. The research applied feedforward propagation neural network (FFNN), emotional neural network (EANN), and conventional linear regression (LR) in the prediction of compressive in which FFNN, EANN, and LR models were trained on the experimental data obtained from addition of 0%–10% RHA and 0%–20% CCW in the SCC mixtures. The results revealed that inclusion of CCW reduces the workability of SCC mixtures and increases in compressive strength at 28 days were observed for SCC mixture containing 10% RHA and 0% CCW against the reference mixtures. The results also indicated that all the AI models (FFNN, EANN, and LR) performed very well with R2-values higher than 0.8951 in both the testing and training stages. The results showed that EANN-M3, FFNN-M3, and LR-M3 combination has the highest performance evaluation criteria of R2 = 0.9733 and 0.9610, R2 = 0.9440 and 0.9454 and R2 = 0.9117 and 0.9205 in both training and testing stages, respectively. It indicates the proposed models' high accuracy in predicting the compressive strength σ of self-compacting concrete with rice husk ash as cement replacement and calcium carbide waste as supplementary materials. The result also suggested that other models, like emerging algorithms, hybrid models, and optimization methods, could enhance the models’ performance.

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Acknowledgements

The authors acknowledge the Department of Civil Engineering Laboratory, Bayero University Kano-Nigeria, for their support throughout this study.

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Author notes

  1. Salim Idris Malami and S.I. Abba contributed equally to this work.

Authors and Affiliations

  1. Department of Civil Engineering, Bayero University, P.M.B. 3011, Kano, Nigeria

    S. I. Haruna & Musa Adamu

  2. School of Civil Engineering, Tianjin University, Tianjin, 300072, China

    S. I. Haruna

  3. Department of Civil Engineering, Kano University of Science and Technology, Wudil, Kano, Nigeria

    Salim Idris Malami

  4. Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, 99138, Nicosia, Turkish Republic of Northern Cyprus

    A. G. Usman

  5. Department of Civil Engineering Technology, Kano State Polytechnic, Kano, Nigeria

    AIB. Farouk

  6. Department of Civil Engineering, Faculty of Engineering, Near East University, Near East Boulevard, Via Mersin 10, 99138, Nicosia, North Cyprus, Turkey

    Shaban Ismael Albrka Ali

  7. Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria

    S. I. Abba

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  1. S. I. Haruna
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Haruna, S.I., Malami, S.I., Adamu, M. et al. Compressive Strength of Self-Compacting Concrete Modified with Rice Husk Ash and Calcium Carbide Waste Modeling: A Feasibility of Emerging Emotional Intelligent Model (EANN) Versus Traditional FFNN. Arab J Sci Eng 46, 11207–11222 (2021). https://doi.org/10.1007/s13369-021-05715-3

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