Abstract
Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values.
Highlights
Emission of carbon dioxide caused by various sectors, including construction, industrial processes, transport, residential, and agriculture, has emerged as a severe problem that dramatically affectsAppl
While the results indicated that all three machine learning approaches could predict fly ash-based geopolymer (FAGP) concrete compressive strength with some degree of accuracy, the residual network (ResNet) model was the most promising method with the highest R2 (0.937) and the lowest root mean square error (RMSE) (1.987) and mean absolute percentage error (MAPE) (6.6) values
This observation was confirmed by additional training and assessment under the K-fold cross validation scheme and paired t-test with α = 0.05, where the highest R2 (0.934 ± 0.021) and the lowest RMSE (2.750 ± 0.573) and MAPE (8.552 ± 1.333) were observed in the ResNet-based model
Summary
Emission of carbon dioxide caused by various sectors, including construction, industrial processes, transport, residential, and agriculture, has emerged as a severe problem that dramatically affectsAppl. Sci. 2020, 10, 7726 global climate change. Calcining limestone in Portland cement production represents 8% of global anthropogenic CO2 emission [1]. Global production of cement increased rapidly from 1.5 billion tonnes in 1998 [2] to 4.1 billion tonnes in 2018 [3], which has significantly impacted emissions linked to the construction sector. This justifies the need for more sustainable alternatives sourced from industrial by-products/wastes with minimal embodied carbon, offering a balance of technical, environmental, and economic benefits
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