Machine Learning Based Reactivity Prediction of Fly Ash Type F Produced from South Korea
Source abstract
Abstract
Abstract Fly ash (FA) is the most commonly used supplementary cementitious material in the world. However, the reactivity of FA varies substantially. In this study, new machine learning (ML) model has been developed to efficiently predict the amorphous content in FA type F. Compared to the existing ML model using types F and C of FA from different countries, this study more focused on the improved prediction of FA type F only produced from South Korea. It was found that the contents of CaO and SiO2 impact high in predicting the amount of aluminosilicate glass. However, the contribution of Al2O3 and Fe2O3 are ranked differently. The improved model algorithm was proposed as a combination of three ensemble techniques of bagging, boosting, and stacking. As a result of the test, the final model shows $${R}2$$ of 0.80 in predicting the amount of aluminosilicate glass in FA type F.
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Research context
OpenAlex topics: Concrete and Cement Materials Research · Recycling and utilization of industrial and municipal waste in materials production · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (70)
Show source-provided references
1. Abualrous Y. (2017). Characterization of Indian and canadian fly ash for use in concrete, PhD. Thesis. University of Toronto
2. Antwarg, R. M. L., Shapira, B., & Rokach, L. (2019). Explaining anomalies detected by autoencoders using SHAP. Arxiv preprint. https://doi.org/10.48550/arXiv.1903.0240710.48550/arXiv.1903.02407 ↗
3. ASTM C618. (2019). Specification for coal fly ash and raw or calcined natural pozzolan for Use in concrete. West Conshohocken: ASTM International.
4. Aughenbaugh, K. L., Stutzman, P., & Juenger, M. C. G. (2016). Identifying glass compositions in fly ash. Frontiers in Materials, 3, 1.10.3389/fmats.2016.00001 ↗
5. Aughenbaugh, K. L., Williamson, T., & Juenger, M. C. G. (2014). Critical evaluation of strength prediction methods for alkali-activated fly ash. Materials and Structures, 48(3), 607–620.10.1617/s11527-014-0496-z ↗
6. Barten, A. P. (1987). The coefficient of determination for regression without a constant term, The Practice of Econometrics (pp. 171–189). Dordrecht: Springer.
7. Bhagath Singh, S. M. A. G. V. P., Kolluru, M. A., & Subramaniam, V. L. (2016). Quantitative XRD analysis of binary blends of siliceous fly ash and hydrated cement. The American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)MT.1943-5533.000155410.1061/(ASCE)MT.1943-5533.0001554 ↗
8. Breiman, L. (1996). Stacked regressions. Machine Learning, 24(1), 49–64.10.1007/BF00117832 ↗
9. Brouwers, H. J. H., & Van Eijk, R. J. (2002). Fly ash reactivity: Extension and application of a shrinking core model and thermodynamic approach. Journal of Materials Science, 37, 2129.10.1023/A:1015206305942 ↗
10. Chancey, R. T., Stutzman, P., Juenger, M. C. G., & Fowler, D. W. (2010). Comprehensive phase characterization of crystalline and amorphous phases of a class F fly ash. Cement and Concrete Research, 40(1), 146–156.10.1016/j.cemconres.2009.08.029 ↗
11. Cho, Y.-H., An, E.-M., Chon, C.-M., & Lee, S. (2016a). Effect of fillers on high temperature shrinkage reduction of geopolymers. Journal of the Korean Institute of Resources Recycling, 25(6), 73–81.10.7844/kirr.2016.25.6.73 ↗
12. Cho, Y.-H., An, E.-M., Lee, S.-J., Chon, C.-M., & Kim, D.-J. (2016b). Influence of fine aggregate properties on unhardened geopolymer concrete. Journal of the Korean Recycled Construction Resources Institute, 4(2), 101–111.10.14190/JRCR.2016.4.2.101 ↗
13. Cho, Y. K., Jung, S. H., & Choi, Y. C. (2019). Effects of chemical composition of fly ash on compressive strength of fly ash cement mortar. Construction and Building Materials, 204, 255–264.10.1016/j.conbuildmat.2019.01.208 ↗
14. Cho, Y. K., & Lee, K. M. (2019). Effect of chemical properties of fly ash on the compressive strength of geopolymer. Construction and Building Materials, 204, 255.10.1016/j.conbuildmat.2019.01.208 ↗
15. Derbeko, P., Yaniv, R. E., & Meir, R. (2022). Variance optimized bagging European conference on machine learning. Berlin: Springer, Berlin Heidelberg.
16. Diamond, S. (1983). On the glass present in low-calcium and in high-calcium flyashes. Cement and Concrete Research, 13(4), 459–464.10.1016/0008-8846(83)90002-9 ↗
17. Donatello, S., Tyrer, M., & Cheeseman, C. R. (2010). Comparison of test methods to assess pozzolanic activity. Cement and Concrete Composites, 32(2), 121–127.10.1016/j.cemconcomp.2009.10.008 ↗
18. Doroudi, S. (2020). The bias-variance tradeoff: How data science can inform educational debates. AERA Open, 6, 2332858420977208.10.1177/2332858420977208 ↗
19. Durdziński, P. T., Ben Haha, M., Bernal, S. A., De Belie, N., Gruyaert, E., Lothenbach, B., Menéndez Méndez, E., Provis, J. L., Schöler, A., Stabler, C., Tan, Z., Villagrán Zaccardi, Y., Vollpracht, A., Winnefeld, F., Zając, M., & Scrivener, K. L. (2017). Outcomes of the RILEM round robin on degree of reaction of slag and fly ash in blended cements. Materials and Structures, 50(2), 1.10.1617/s11527-017-1002-1 ↗
20. Durdziński, P. T., Dunant, C. F., Haha, M. B., & Scrivener, K. L. (2015). A new quantification method based on SEM-EDS to assess fly ash composition and study the reaction of its individual components in hydrating cement paste. Cement and Concrete Research, 73, 111–122.10.1016/j.cemconres.2015.02.008 ↗
21. Fowler DDW. (2013) Characterizing fly ash, center for transportation research.
22. Geman, B. E. S., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4(1), 1–58.10.1162/neco.1992.4.1.1 ↗
23. Göktepe, A. B., Sezer, A., Sezer, G. İ, & Ramyar, K. (2008). Classification of time-dependent unconfined strength of fly ash treated clay. Construction and Building Materials, 22(4), 675–683.10.1016/j.conbuildmat.2006.10.008 ↗
24. Hasanbeigi, A., Price, L., & Lin, E. (2012). Emerging energy-efficiency and CO2 emission-reduction technologies for cement and concrete production: A technical review. Renewable and Sustainable Energy Reviews, 16(8), 6220–6238.10.1016/j.rser.2012.07.019 ↗
25. James, C. H., & Maria, M. (2001). An Approach toward a combined scheme for the petrographic classification of fly ash. Energy & Fuels, 15, 1319.10.1021/ef010146z ↗
26. Jang, J. G., & Lee, H. K. (2016). Effect of fly ash characteristics on delayed high-strength development of geopolymers. Construction and Building Materials, 102, 260–269.10.1016/j.conbuildmat.2015.10.172 ↗
27. Jeon, D., Jun, Y., Jeong, Y., & Oh, J. E. (2015). Microstructural and strength improvements through the use of Na2 CO3 in a cementless Ca(OH)2 -activated Class F fly ash system. Cement and Concrete Research, 67, 215–225.10.1016/j.cemconres.2014.10.001 ↗
28. Jeon, D., Yum, W. S., Jeong, Y., & Oh, J. E. (2018). Properties of quicklime(CaO)-activated Class F fly ash with the use of CaCl2. Cement and Concrete Research, 111, 147–156.10.1016/j.cemconres.2018.05.019 ↗
29. John MF. (2017). Fly ash classification—old and new ideas.
30. Jung-Il, S., Yum, W. S., Sim, S., Park, H.-G., & Oh, J. E. (2020). Effect of magnesium formate as compared with magnesium oxide on the strength enhancement and microstructures of CaO-activated Class F fly ash system. Construction and Building Materials, 253, 119140.10.1016/j.conbuildmat.2020.119140 ↗
31. Kang, N.-H., Chon, C.-M., Jou, H.-T., & Lee, S. (2013). Effect of particle size and unburned carbon content of fly ash from hadong power plant on compressive strength of geopolymers. Korean Journal of Materials Research, 23(9), 510–516.
32. Kim, B., Heo, Y.-E., Chon, C.-M., & Lee, S.-J. (2018). Influence of Na/Al ratio and curing temperature of geopolymers on efflorescence reduction. Resources Recycling, 27(6), 59.
33. Kim, Y., Kim, K., & Jeong, G.-Y. (2017). Study of detailed geochemistry of hazardous elements in weathered coal ashes. Fuel, 193, 343–350.10.1016/j.fuel.2016.12.080 ↗
34. Lee SI, Lundberg SM. 2017. A unified approach to interpreting model predictions, Advances in neural information processing systems. p. 4765–4774.
35. Mangalathu, S., Hwang, S.-H., & Jeon, J.-S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927.10.1016/j.engstruct.2020.110927 ↗
36. Matias, T., Souza, F., Araújo, R., & Antunes, C. H. (2014). Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing, 129, 428–436.10.1016/j.neucom.2013.09.016 ↗
37. Moomen, M., & Siddiqui, C. (2022). Probabilistic deterioration modeling of bridge component condition with random effects. Journal of Structural Integrity and Maintenance, 7(3), 151–160.10.1080/24705314.2022.2048244 ↗
38. Moon, G. D., Oh, S., & Choi, Y. C. (2016). Effects of the physicochemical properties of fly ash on the compressive strength of high-volume fly ash mortar. Construction and Building Materials, 124, 1072–1080.10.1016/j.conbuildmat.2016.08.148 ↗
39. Moreno, N., Querol, X., Andres, J., Stanton, K., Towler, M., Nugteren, H., Janssenjurkovicova, M., & Jones, R. (2005). Physico-chemical characteristics of European pulverized coal combustion fly ashes. Fuel, 84(11), 1351–1363.10.1016/j.fuel.2004.06.038 ↗
40. Mukhopadhyay, A. K., Liu, K.-W., & Jalal, M. (2019). An innovative approach to fly ash characterization and evaluation to prevent alkali-silica reaction. ACI Materials Journal. https://doi.org/10.14359/5171675110.14359/51716751 ↗
41. Oey, T., Timmons, J., Stutzman, P., Bullard, J. W., Balonis, M., Bauchy, M., & Sant, G. (2017). An improved basis for characterizing the suitability of fly ash as a cement replacement agent. Journal of the American Ceramic Society, 100(10), 4785–4800.10.1111/jace.14974 ↗
42. Oh, J. E., Jun, Y., & Jeong, Y. (2014). Characterization of geopolymers from compositionally and physically different Class F fly ashes. Cement and Concrete Composites, 50, 16–26.10.1016/j.cemconcomp.2013.10.019 ↗
43. Oh, J. E., Jun, Y., Jeong, Y., & Monteiro, P. J. M. (2015). The importance of the network-modifying element content in fly ash as a simple measure to predict its strength potential for alkali-activation. Cement and Concrete Composites, 57, 44–54.10.1016/j.cemconcomp.2014.12.001 ↗
44. Paris, J. M., Roessler, J. G., Ferraro, C. C., DeFord, H. D., & Townsend, T. G. (2016). A review of waste products utilized as supplements to Portland cement in concrete. Journal of Cleaner Production, 121, 1–18.10.1016/j.jclepro.2016.02.013 ↗
45. Park, B., & Choi, Y. C. (2019). Prediction of self-healing potential of cementitious materials incorporating crystalline admixture by isothermal calorimetry. International Journal of Concrete Structures and Materials. https://doi.org/10.1186/s40069-019-0349-910.1186/s40069-019-0349-9 ↗
46. Pietersen, H., Fraay, A., & Bijen, J. (1989). Reactivity of fly ash at high pH, in fly ash and coal conversion by-products: Characterization, utilization and disposal VI. Materials research society symposium proceedings.
47. Saraber A. (2017). Fly ash from coal and biomass for use in concrete Origin, properties and performance.
48. Schocken, A. G. S. (1991). Neural networks for decision support systems: problems and opportunities center for research on information systems. New York: Stern School of Business, New York University.
49. Schwenk, B. Y. H. (2000). Boosting neural networks. Neural Computation, 12(8), 1869–1887.10.1162/089976600300015178 ↗
50. Sheare, C. R. (2014). The Productive reuse of coal, biomass and co-fired fly ash. ACI Materials Journal. https://doi.org/10.14359/5168682710.14359/51686827 ↗
51. Shi, C., & Zheng, K. (2007). A review on the use of waste glasses in the production of cement and concrete. Resources, Conservation and Recycling, 52(2), 234–247.10.1016/j.resconrec.2007.01.013 ↗
52. Shon, C. S. (2004). Testing the effectiveness of class C and class F fly ash in controlling expansion due to alkali-silica reaction using modified ASTM C 1260 test method. Journal of Materials in Civil Engineering, 16(1), 20.10.1061/(ASCE)0899-1561(2004)16:1(20) ↗
53. Sindhunata, J. S. J., van Deventer, G. C., & Lukey, H. X. (2006). Effect of curing temperature and silicate concentration on fly-ash-based geopolymerization. Industrial & Engineering Chemistry, 45, 3559.10.1021/ie051251p ↗
54. Singh, G. B., & Subramaniam, K. (2018). Characterization of Indian fly ashes using different experimental techniques. Indian Concrete Journal, 92, 10.
55. Snellings, R., & Scrivener, K. L. (2016). Rapid screening tests for supplementary cementitious materials: Past and future. Materials and Structures, 49(8), 3265–3279.10.1617/s11527-015-0718-z ↗
56. Song, Y., Yang, K., Chen, J., Wang, K., Sant, G., & Bauchy, M. (2021). Machine learning enables rapid screening of reactive fly ashes based on their network topology. ACS Sustainable Chemistry & Engineering, 9(7), 2639–2650.10.1021/acssuschemeng.0c06978 ↗
57. Suárez-Ruiz, I., Valentim, B., Borrego, A. G., Bouzinos, A., Flores, D., Kalaitzidis, S., Malinconico, M. L., Marques, M., Misz-Kennan, M., Predeanu, G., Montes, J. R., Rodrigues, S., Siavalas, G., & Wagner, N. (2017). Development of a petrographic classification of fly-ash components from coal combustion and co-combustion (An ICCP Classification System, Fly-Ash working group—commission III.). International Journal of Coal Geology, 183, 188–203.10.1016/j.coal.2017.06.004 ↗
58. Suh JI, Park HG. 2019 Development of one-part lime-activated fly ash binders with formate and nitrate compounds.
59. Suh, J.-I., Yum, W. S., Song, H., Park, H.-G., & Oh, J. E. (2019). Influence of calcium nitrate and sodium nitrate on strength development and properties in quicklime (CaO)-activated Class F fly ash system. Materials and Structures. https://doi.org/10.1617/s11527-019-1413-210.1617/s11527-019-1413-2 ↗
60. Sumer, M. (2012). Compressive strength and sulfate resistance properties of concretes containing class F and class C fly ashes. Construction and Building Materials, 34, 531–536.10.1016/j.conbuildmat.2012.02.023 ↗
61. Tangri, N., Ansell, D., & Naimark, D. (2008). Predicting technique survival in peritoneal dialysis patients: Comparing artificial neural networks and logistic regression. Nephrology, Dialysis, Transplantation, 23(9), 2972–2981.10.1093/ndt/gfn187 ↗
62. Vassilev, S. V., & Vassilev, C. G. (1996). Occurrence, abundance and origin of minerals in coals and coal ashes. Fuel Processing Technology, 48, 85.10.1016/S0378-3820(96)01021-1 ↗
63. Ward, C., & French, D. (2006). Determination of glass content and estimation of glass composition in fly ash using quantitative X-ray diffractometry. Fuel, 85(16), 2268–2277.10.1016/j.fuel.2005.12.026 ↗
64. Wardhono, A. (2017). Comparison study of class F and class C fly ashes as cement replacement material on strength development of non-cement mortar. Materials Science ans Engineering, 288, 012019.
65. Williams, R. P., & van Riessen, A. (2010). Determination of the reactive component of fly ashes for geopolymer production using XRF and XRD. Fuel, 89(12), 3683–3692.10.1016/j.fuel.2010.07.031 ↗
66. Wolpert, H. D. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.10.1016/S0893-6080(05)80023-1 ↗
67. Xu, G., & Shi, X. (2018). Characteristics and applications of fly ash as a sustainable construction material: A state-of-the-art review. Resources, Conservation and Recycling, 136, 95–109.10.1016/j.resconrec.2018.04.010 ↗
68. Yang, X., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019). Concepts of artificial intelligence for computer-assisted drug discovery. Chemical Reviews, 119(18), 10520–10594.10.1021/acs.chemrev.8b00728 ↗
69. Yoon, I.-S., Chang, C., & Nam, J.-W. (2022). Effect of carbonation on chloride transportation parameters in cementitious materials. Journal of Structural Integrity and Maintenance, 7(3), 161–167.10.1080/24705314.2022.2048245 ↗
70. Zachariasen, W. H. (1932). The Atomic arrangement in glass. Journal of the American Chemical Society, 54, 3841–3851.10.1021/ja01349a006 ↗