1 Introduction
Limestone (CaCO
3) is used as a raw material in the manufacturing of cement, a main binding component of concrete. CaCO
3 completely decomposes at around 800 ℃, emitting a large amount of CO
2. In producing 1 ton of cement, approximately 0.8 ton of CO
2 is emitted. This amounts to about 5–8% of global CO
2 emissions (Hasanbeigi et al.
2012). As carbon neutrality has emerged as a global concern, there has been a growing interest in researching supplementary cementitious materials (SCMs) as substitutes for ordinary Portland cement (Paris et al.
2016). Due to its abundance as an industrial byproduct and its ability to enhance the quality of concrete, FA has become a popular material as an SCM, offering significant economic benefits. However, the quality of FA varies greatly depending on the facilities and operating conditions of coal-fired power plants, and on the types of raw coal (Xu and Shi
2018). Since the quality of FA has a significant impact on concrete performance (Chancey et al.
2010; Oey et al.
2017), it is critical to judge the material properties of FA before being used in cement-based materials. However, according to ASTM standards, FA is simply classified as C-class or F-class according to the CaO content. Many studies, however, have found that such classifications are inaccurate (Göktepe et al.
2008; James and Maria
2001; John
2017; Suárez-Ruiz et al.
2017). These claims can be also supported by a recent research showing that the strength development of concrete is more influenced by the complex reactivity of FA, rather than solely by its CaO content (Donatello et al.
2010; Snellings and Scrivener
2016).
Although the chemical composition of FA significantly varies depending on the raw coal type, the mineral composition of the crystalline phase primarily consists of quartz (SiO
2) and mullite (3Al
2O
32SiO
2). Meanwhile, the amorphous (noncrystalline) phase exists in the range of 40–80 wt.% in FA (Vassilev and Vassilev
1996). FA’s reactivity is generally governed by its amorphous phase, because the crystalline phase does not actively participate in the reaction (Ward and French
2006; Williams and Riessen
2010). It is recognized that the component with the greatest reactivity in the amorphous content is aluminosilicate glass, which is a combination of alumina (Al
2O
3) glass and silicate (SiO
2) glass (Brouwers and Eijk
2002; Moomen and Siddiqui
2022; Pietersen et al.
1989; Sindhunata et al.
2006). As a result, estimating the quantity of aluminosilicate glass (amorphous aluminosilicate) is critical in predicting the strength of FA contained concrete. The amorphous component of FA could be indirectly analyzed using quantitative x-ray diffraction (QXRD). This analysis applies the partial or known crystal structures (PONKCS) method, which defines an unknown mineral phase as a virtual crystal structure and quantitatively analyzes its mixture with other minerals using the Rietveld method (Kim et al.
2018). The chemical composition of FA, on the other hand, can be simply obtained by quick XRF analysis.
According to certain studies, the amorphous phase composition of FA is correlated with (but not identical to) chemical compostion of bulk FA (i.e., cystalline and amorphous phase) (Aughenbaugh et al.
2016; Xu and Shi
2018). However, mapping results between the amorphous phase composition and chemical composition of bulk FA remains uncertain. Such linkages could be proposed by machine learning (ML) technique. QXRD analysis of mineralogical phase composition has limits in that a skilled experimenter is needed and the process is rather difficult considering the easy implementation of XRF test. Thus, the fact that such mapping can be produced by ML is significant in and of itself. Furthermore, even in the absence of an exact solution about this mapping, ML has a potential to rapidly predict the amoutn of amorphous phase from the quick XRF data.
Meanwhile, most FA-related research focuses solely on concrete compression strength and durability, which definitely vary according to the content or type of FA. There has been little research on the ML to forecast the chemical reactivity or structure of FA. This attempt was recently reported by Song et al. (
2021). This paper firstly attempted to predict the chemical component of amorphous phase (i.e., calculated by the QXRD) from the XRF-based chemical compositions of FA. However, considering the number and characteristics of used FA data, application of ANN(artificial neural network) algorithm may have certain limitations as they concluded so in the paper. The motivation of this study is from the inaccruate prediction result of exising ML model on the FA type F from Korea. FA itself has high complexity and it property (i.e., chemical compatibility with cement-based materials) should be greatly influenced by its geographical origin and the operational conditions of thermal power plants (Cho and Lee
2019). Therefore, it is not surprising that the existing ML established based on interntional database of FA both F and C was not able to accurately predict the reactivity of FA type F from a certain country. Furthermore, it is well known that the hydration mechanism of FA type F and C is different in cementitious materials (Shon
2004; Sumer
2012; Wardhono
2017; Yoon et al.
2022). Therefore, it is rational to separate the type F and C for constructing reliable ML model. This study aims to propose a modified ML to tackle the issue. The performance of the new ML model is evaulated by how accurately the target value (aluminosilicate glass content estimated by QXRD) of the given data set (the test set) was predicted (i.e.,
\({R}^{2}\) value). First, we recreated the ML model (ANN) of Song et al. (
2021). The model was made using FA from various countries (i.e., the United States, India, Canada, the Netherlands, Spain, Greece, and Italy). Then, Korean FA was added as new data set to validate the model’s applicability in Korea. Second, another ML model was built using only Korean FA type F. Third, this new model has been refined and validated.
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