Prediction of alkali–silica reaction expansions of mortars containing glass waste

Abstract

The majority of existing findings regarding expansion (EXP) risks in concretes containing waste glass stem from experimental studies. There is a need for rapid assessment methods to ensure safer recycling of glass waste in cementitious composites. In this study, an artificial neural network (ANN) model was developed to accurately predict alkali–silica reaction EXP/mitigation resulting from the integration of glass waste in mortars. The analysis considered glass incorporation either separately as waste glass powder (WGP) and waste glass aggregates (WGAs), or in combination, at contents of up to 100% for WGA and 30% for WGP. A set of 175 mixtures was analysed, considering five distinct variables, which encompassed different mix proportions, involving varying components of cement, natural aggregates, WGP and WGA, in addition to the duration of environmental exposure. The results show that the EXP of WGA mortars decreased with the increased incorporation of WGP. The EXP values obtained from validation and experience confirm the high accuracy of the developed ANN model, with validation coefficients reaching up to 98.061% and a small value of the mean square error.