Evaluation of Compressive Strength of Fibre Reinforced Concrete using Randomizable Filtered Classifier
Abideen Adekunle GANIYU
Abstract. Fibre reinforced concrete (FRC) is a matrix material that adds discrete fibres to mixed concrete with the aim of increasing its mechanical properties. Conventional techniques for the evaluation of compressive strength of concrete entail time-consuming, laborious laboratory tests. Machine Learning (ML) models have the capabilities to utilise data on mix proportions to train a model that can accurately estimate strength outcomes for new mixes. This research employs a codeless ML tool to predict the compressive strengths of FRC using 173 datapoints with 7 input features of binder, water, fine aggregate, coarse aggregate, volume fraction of steel, aspect ratio and age. An 80:20 data split was adopted for training and testing sets, while pre-processing of data, visualisation, hyper-parameter optimisation, and feature selection were all done in the model development stage. Randomizable Filtered Classifier (RFC) algorithm yielded the optimal model, with a coefficient of determination, R2 of 0.9886 and root-mean-square-error of 14.4529 as values of the performance measures. This implies that the model’s predictions are closely aligned with the true values, underscores the strong correlation between the input features and the compressive strength, and asserts the model’s validity. The study leverages data-driven soft computing methods to provide timely, cost-effective, accurate and reliable processes for the design of compressive strength of fibre-reinforced concrete for practical usage.
Keywords
Fibre-reinforced Concrete, Compressive Strength, Machine Learning, Randomizable Filtered Classifier
Published online 4/2/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Abideen Adekunle GANIYU, Evaluation of Compressive Strength of Fibre Reinforced Concrete using Randomizable Filtered Classifier, Materials Research Proceedings, Vol. 63, pp 47-55, 2026
DOI: https://doi.org/10.21741/9781644904053-6
The article was published as article 6 of the book Advances in Cement and Concrete Research
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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