City Research Online

Abstract

In cold regions, using steel fibres (SF) and manufactured volcanic scoria sand (MVSS) for fibre-reinforced concrete formulation can mitigate the problem of deterioration of mechanical properties due to freeze–thaw (F–T) cycles and effectively reduces the consumption of natural sand. To better predict the residual mechanical properties of concrete after freeze–thaw (F–T) cycles, this study develops four machine learning models: back-propagation neural network, convolutional neural network, decision tree and CatBoost. The input variables were water/cement ratio, manufactured volcanic scoria sand replacement rate, steel fibre volume content and F–T cycles. The output values were compressive strength (CS) and splitting tensile strength (STS). Key indicators showed all models exhibited acceptable accuracy. CatBoost outperformed the other methods with root mean squared error of 0.391 and 0.037, mean absolute error of 0.273 and 0.026, mean absolute percentage error of 0.009 and 0.011, scatter index of 0.011 and 0.014, and index of agreement of 0.999 and 0.999 for CS and STS, respectively. The coefficients of determination (R2) are all as high as 0.99. CatBoost shows the highest prediction accuracy. Sensitivity testing of the strength of steel-fibre-reinforced manufactured volcanic scoria sand concrete using CatBoost and it showed F–T cycles were an essential parameter. Finally, scanning electron microscopy shows SF and MVSS can improve the frost resistance of concrete.

Publication Type:	Article
Additional Information:	© 2025 Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.
Publisher Keywords:	fibre-reinforced concrete, freeze-thaw, machine learning, mechanical properties, volcanic scoria
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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