Abstract
To find the best method for predicting the compressive strength of concrete mix utilizing waste marble powder, the potential of six machine learning algorithms, namely random forest, random tree, Gaussian process, support vector machines, adaptive neuro-fuzzy inference system, and artificial neural networks, was evaluated in this study. The study compares the results obtained by the aforementioned methodologies for a specific dataset. The entire dataset had 158 readings containing input variables such as cement, fine aggregates, coarse aggregates, water, waste marble powder, and curing days, while the compressive strength of the concrete mix was the output variable. Statistics assessment criteria were used to determine the effectiveness of the approaches. The results show that the support vector machine technique, with its short error bandwidth, outperforms other approaches for predicting the compressive strength of concrete, including waste marble powder. The support vector machine process predicts superior results with a higher coefficient of correlation (0.9415), the lowest mean absolute error (1.5372), and a root mean square error value of 2.1092. However, as compared to time-consuming laboratory work, the support vector machine modeling approach appears to be more cost-effective and easier. Based on sensitivity analysis, curing days were found to be one of the most important factors in predicting the compressive strength of a concrete mix, followed by marble powder and fine aggregates.
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Sharma, N., Thakur, M.S., Upadhya, A. et al. Soft computing techniques for assessment of strength of concrete with marble powder. Multiscale and Multidiscip. Model. Exp. and Des. 6, 81–96 (2023). https://doi.org/10.1007/s41939-022-00130-z
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DOI: https://doi.org/10.1007/s41939-022-00130-z