article · Case Studies in Construction Materials
Researchers investigated the development of artificial geopolymer aggregates by substituting fine aggregates with crumb rubber, plastic waste, and granulated cork. The study examined the effects of these waste additions on density, compressive strength, and water absorption. Advanced machine learning models were applied alongside response surface methodology to optimise the mix, with Gaussian process regression delivering the highest predictive accuracy for aggregate density and strength. Incorporating waste materials reduced both density and compressive strength compared to control mixtures. Microstructural assessments revealed that curing at 60 and 90 degrees Celsius maintained a denser matrix, whereas heating to 500 degrees Celsius caused microcracking and reduced binder gel formation. An optimal formulation of rubber, plastic, and cork yielded lightweight aggregates with a compressive strength of 4.95 megapascals and low water absorption when cured at 60 degrees Celsius.
Finding ways to incorporate discarded tyres, plastics, and cork into building supplies reduces landfill burden and limits the extraction of natural sand and gravel. This research demonstrates how predictive computational models can support the formulation of lightweight artificial aggregates, helping engineers balance the trade-offs between high waste utilisation and acceptable mechanical performance for sustainable construction.
This work is of interest to construction material manufacturers and concrete producers seeking lightweight aggregate alternatives derived from industrial and consumer waste. It enables targeted formulation design using predictive models. Currently at the stage of early-stage laboratory research, the aggregate formulation and curing regimes require pilot-scale validation and structural performance trials before practical adoption can occur.
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This study investigated the effect of incorporating crumb rubber (CR), plastic waste (PW), and granulated cork (GC) as partial substitutes for fine aggregates on the density, compressive strength (CS), and water absorption of artificial geopolymer aggregates (AGAs). The properties of AGAs were evaluated, and the optimal utilization of waste materials was determined using response surface methodology (RSM) and machine learning models, including linear regression (LR), support vector regression (SVR), and Gaussian process regression (GPR). Additionally, the microstructure of the optimized samples was analyzed under varying curing temperatures (60 °C, 90 °C, and 500 °C). The results indicated that the inclusion of these waste materials led to a reduction in both the density and CS of the AGAs compared to the control mixtures. Among the machine learning models, the GPR model demonstrated superior performance, with R² values of 0.989 for density prediction and 0.937 for CS prediction. The GPR model consistently outperformed the LR, SVR, and RSM models, showing higher accuracy in predicting both density and CS. Microstructural analysis revealed significant differences in the AGAs cured at different temperatures, with denser microstructures observed at 60 °C and 90 °C compared to 500 °C. Exposure to higher temperatures resulted in the formation of microcracks and a decrease in the N-A-S-H gel content, leading to lower dry density values. The optimal blend of waste materials, comprising 146 kg/m³ of CR, 46.80 kg/m³ of PW, and 5.25 kg/m³ of GC, resulted in AGAs with minimum densities of 1268.17 kg/m³ , 1194.40 kg/m³ , and 732.80 kg/m³ at curing temperatures of 60 °C, 90 °C, and 500 °C, respectively. Additionally, these AGAs exhibited a CS of 4.95 MPa and a water absorption rate of 10.37 % after curing at 60 °C.
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DOI: 10.1016/j.cscm.2025.e04725
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