Strength-based life cycle CO2 of fly ash concrete: Variation with common mix parameters

Abstract

The addition of fly ash (FA) to concrete will not only reduce the CO2 content of concrete but also affect the compressive strength of concrete. However, most studies on carbon emissions have underestimated the complexity of the relationship of emissions with the concrete mix design and compressive strength. In this study, a database of mix designs was established by considering 1062 datasets related to the laboratory concrete mix designs and compressive strength values, and by using an artificial neural network model, a multiple nonlinear regression model, random forest model and support vector machines to simulate and predict the compressive strength of concrete. Moreover, the differences in the life-cycle CO2 corresponding to FA concrete among three different allocation types-no, mass, and economic value allocations-based on two functional units (FU)-per unit volume and per unit volume and strength were investigated. Finally, sustainable mix design method of FA concrete was established to find the optimal FAC mixtures and the correlation between CO2 emission and concrete mix parameters was analyzed by the maximal information correlation (MIC). The results indicate that the neural network model has a better regression effect than the multiple nonlinear regression model and other machine learning models on the prediction of compressive strength and CO2 emissions. For different functional units, the CO2 emissions under FU1 increases with the strength of concrete regardless of different allocation types; the CO2 emissions under FU2 varies considerably with changes in the amount of added FA under no allocation and economic value allocation. The CO2 emission under FU1 has a stronger correlation with various mix parameters than that under FU2.