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The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings

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Abstract

Sustainable building construction requirements demand an efficient utilization of industrial waste as alternatives to construction materials. In this work, the nano-silica (NS) has been used as a precursor to the compressive strength of mortar and multiple mixes of 107 were produced to study the effect of the nano-silica precursor (NSP). Advanced Machine Learning (AML) techniques have been used in this research work to predict the compressive strength of the NSP mortar using 75% to 25% ratio to train and validate the models. The NS precursor with a 17% degree of importance played a substantial influence with the cement due to its contribution to the pozzolanic reaction to produce C-S-H gel in mortar. The accuracies of the developed models were compared using Taylor charts, and also, the variance distribution for the developed models was conducted. The models’ performance indices, mean average error (MAE), mean squared error (MSE), root mean square error (RMSE), sum of squared error (SSE), and the coefficient of determination (R2), were used to decide the superior model. At the end of the exercise, it has been shown that the GP model showed a poorly performed model with outliers from the NS precursor mortar UCS data entries outside the ± 25% envelop. The parametric line fit of the GP is y = 0.973x, which produced MAE of 5.62 MPa, MSE of 46.71 MPa, RMSE of 6.83 MPa, and R2 of 0.680. Also, the EPR model showed a parametric line fit of y = 0.983x, which produced MAE of 4.10 MPa, MSE of 29.67 MPa, RMSE of 5.45 MPa, and R2 of 0.823, while the most superior model was produced by the ANN with a parametric line fit of 0.997x, which produced MAE of 1.47 MPa, MSE of 3.84 MPa, RMSE of 1.96 MPa, and R2 of 0.980. The outperformance of the ANN over the other AI techniques is supported by previous research work even though the ANN did not produce a closed-form parametric expression that allows a manual application of the model in the design and construction of buildings with mortar under NS precursor effect. Generally, the NSP has shown a reliable potential to improve the hardened strength of mortar, which confirms its application in the built environment as a sustainable pozzolanic construction material.

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Authors and Affiliations

  1. Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria

    Kennedy C. Onyelowe

  2. Department of Civil Engineering, University of the Peloponnese, 26334, Patras, Greece

    Kennedy C. Onyelowe

  3. Department of Civil Engineering, Future University in Egypt, New Cairo, Egypt

    Ahmed M. Ebid

  4. Civil Engineering Department, Al-Balqa Applied University, As-Salt, Jordan

    Shadi Hanandeh

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  1. Kennedy C. Onyelowe
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  2. Ahmed M. Ebid
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Contributions

K.C.O. conceptualized and supervised the project. K.C.O., A.M.E. and S.H. wrote the main manuscript texts and K.C.O. and A.M.E. prepared the figures. All authors reviewed the manuscript.

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Correspondence to Kennedy C. Onyelowe.

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Appendix

Appendix

See Tables 1, 2, 3, 4, 5.

Table 1 Utilized database of the mortar with nano-silica precursor
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Table 2 Statistical analysis of collected database
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Table 3 Pearson correlation matrix
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Table 4 Weights matrix for the developed ANN
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Table 5 Accuracies of developed models for the UCS of mortar with NS precursor
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Onyelowe, K.C., Ebid, A.M. & Hanandeh, S. The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings. Asian J Civ Eng 25, 1135–1148 (2024). https://doi.org/10.1007/s42107-023-00832-w

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