Data-Driven Insights into Controlling the Reactivity of Supplementary Cementitious Materials in Hydrated Cement
Source abstract
Abstract
Abstract Supplementary cementitious materials (SCMs) play an essential role in sustainable construction due to their potential to reduce carbon emissions, promote circular economy principles, and enhance the properties of concrete. However, the inherent diversity of SCMs makes it challenging to predict their degree of reaction (DOR). This study applies machine learning techniques to predict DOR while exploring key parameters affecting it. Five machine learning models are utilized: linear regression, Gaussian process regression (GPR), decision tree regression, support vector machine and extreme gradient boosting, with GPR providing the most accurate and adaptable prediction. The study delves into the impact of various parameters on DOR, revealing their significance. Silica content emerges as the most critical, followed by particle size distribution, specific gravity, and water-to-cement (W/C) ratio. Optimizing DOR requires extending curing time, reducing particle size distribution, and considering optimal silica content and W/C ratio. This research emphasizes the importance of understanding the relationships between parameters and the DOR of SCMs, providing insights to enhance the efficiency of SCMs in cementitious systems through machine learning and data-driven analysis.
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Research context
OpenAlex topics: Concrete and Cement Materials Research · Concrete Properties and Behavior · Magnesium Oxide Properties and Applications
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References (62)
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1. Abuodeh, O. R., Abdalla, J. A., & Hawileh, R. A. (2020). Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques. Applied Soft Computing Journal, 95, 106552. https://doi.org/10.1016/j.asoc.2020.10655210.1016/j.asoc.2020.106552 ↗
2. Ahmed, A. (2019). Chemical reactions in pozzolanic concrete. Modern Approaches on Material Science, 1(4), 128–133. https://doi.org/10.32474/mams.2019.01.00012010.32474/mams.2019.01.000120 ↗
3. Anurag, Kumar, R., Goyal, S., & Srivastava, A. (2021). A comprehensive study on the influence of supplementary cementitious materials on physico-mechanical, microstructural and durability properties of low carbon cement composites. Powder Technology, 394, 645–668. https://doi.org/10.1016/j.powtec.2021.08.08110.1016/j.powtec.2021.08.081 ↗
4. Arvaniti, E. C., & De Belie, N. (2014). Method development for the particle size analysis. In XIII conference on durability of building materials and components (pp. 679–686).
5. Barnwal, A., Cho, H., & Hocking, T. (2022). Survival regression with accelerated failure time model in XGBoost. Journal of Computational and Graphical Statistics, 31(4), 1292–1302. https://doi.org/10.1080/10618600.2022.206754810.1080/10618600.2022.2067548 ↗
6. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-201410.5194/gmd-7-1247-2014 ↗
7. Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt2016510.38094/jastt20165 ↗
8. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, 13–17 August (pp. 785–794). https://doi.org/10.1145/2939672.293978510.1145/2939672.2939785 ↗
9. Chen, Z., Lin, J., Sagoe-Crentsil, K., & Duan, W. (2022). Development of hybrid machine learning-based carbonation models with weighting function. Construction and Building Materials, 321(November 2021), 126359. https://doi.org/10.1016/j.conbuildmat.2022.12635910.1016/j.conbuildmat.2022.126359 ↗
10. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.62310.7717/PEERJ-CS.623 ↗
11. Chou, J. S., Tsai, C. F., Pham, A. D., & Lu, Y. H. (2014). Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building Materials, 73, 771–780. https://doi.org/10.1016/j.conbuildmat.2014.09.05410.1016/j.conbuildmat.2014.09.054 ↗
12. Cohen, S., Ruppin, E., & Dror, G. (2005). Feature selection based on the shapley value. In IJCAI international joint conference on artificial intelligence (pp. 665–670).
13. de Azevedo Basto, P., Estolano de Lima, V., & de Melo Neto, A. A. (2022). Effect of curing temperature in the relative decrease peak intensity of calcium hydroxide pastes for assessing pozzolanicity of supplementary cementitious materials. Construction and Building Materials, 325(November 2021), 126767. https://doi.org/10.1016/j.conbuildmat.2022.12676710.1016/j.conbuildmat.2022.126767 ↗
14. Diaz-Loya, I., Juenger, M., Seraj, S., & Minkara, R. (2019). Extending supplementary cementitious material resources: Reclaimed and remediated fly ash and natural pozzolans. Cement and Concrete Composites, 101, 44–51. https://doi.org/10.1016/j.cemconcomp.2017.06.01110.1016/j.cemconcomp.2017.06.011 ↗
15. Donatello, S., Tyrer, M., & Cheeseman, C. R. (2010). Comparison of test methods to assess pozzolanic activity. Cement and Concrete Composites, 32(2), 121–127. https://doi.org/10.1016/j.cemconcomp.2009.10.00810.1016/j.cemconcomp.2009.10.008 ↗
16. Durdziński, P. T., Ben Haha, M., Bernal, S. A., De Belie, N., Gruyaert, E., Lothenbach, B., et al. (2017). Outcomes of the RILEM round robin on degree of reaction of slag and fly ash in blended cements. Materials and Structures/Materiaux et Constructions, 50(2), 1–15. https://doi.org/10.1617/s11527-017-1002-110.1617/s11527-017-1002-1 ↗
17. Escalante, J. I., Gómez, L. Y., Johal, K. K., Mendoza, G., Mancha, H., & Méndez, J. (2001). Reactivity of blast-furnace slag in Portland cement blends hydrated under different conditions. Cement and Concrete Research, 31(10), 1403–1409. https://doi.org/10.1016/S0008-8846(01)00587-710.1016/S0008-8846(01)00587-7 ↗
18. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.10.1214/aos/1013203451 ↗
19. Gholami, R., & Fakhari, N. (2017). Support vector machine: Principles, parameters, and applications. Handbook of neural computation (1st ed.). Elsevier Inc. https://doi.org/10.1016/B978-0-12-811318-9.00027-210.1016/B978-0-12-811318-9.00027-2 ↗
20. Gupta, S., & Chaudhary, S. (2022). State of the art review on supplementary cementitious materials in India—II: Characteristics of SCMs, effect on concrete and environmental impact. Journal of Cleaner Production, 357(April), 131945. https://doi.org/10.1016/j.jclepro.2022.13194510.1016/j.jclepro.2022.131945 ↗
21. Haha, M. B., De Weerdt, K., & Lothenbach, B. (2010). Quantification of the degree of reaction of fly ash. Cement and Concrete Research, 40(11), 1620–1629. https://doi.org/10.1016/j.cemconres.2010.07.00410.1016/j.cemconres.2010.07.004 ↗
22. Hallet, V., De Belie, N., & Pontikes, Y. (2020). The impact of slag fineness on the reactivity of blended cements with high-volume non-ferrous metallurgy slag. Construction and Building Materials, 257, 119400. https://doi.org/10.1016/j.conbuildmat.2020.11940010.1016/j.conbuildmat.2020.119400 ↗
23. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. The elements of statistical learning (2nd ed., Vol. 27). Springer Science & Business Media.10.1007/978-0-387-84858-7 ↗
24. Juenger, M. C. G., & Siddique, R. (2015). Recent advances in understanding the role of supplementary cementitious materials in concrete. Cement and Concrete Research, 78, 71–80. https://doi.org/10.1016/j.cemconres.2015.03.01810.1016/j.cemconres.2015.03.018 ↗
25. Juenger, M. C. G., Snellings, R., & Bernal, S. A. (2019). Supplementary cementitious materials: New sources, characterization, and performance insights. Cement and Concrete Research, 122(May 2019), 257–273. https://doi.org/10.1016/j.cemconres.2019.05.00810.1016/j.cemconres.2019.05.008 ↗
26. Kocaba, V., Gallucci, E., & Scrivener, K. L. (2012). Methods for determination of degree of reaction of slag in blended cement pastes. Cement and Concrete Research, 42(3), 511–525. https://doi.org/10.1016/j.cemconres.2011.11.01010.1016/j.cemconres.2011.11.010 ↗
27. Li, X., Snellings, R., Antoni, M., Alderete, N. M., Ben Haha, M., Bishnoi, S., et al. (2018). Reactivity tests for supplementary cementitious materials: RILEM TC 267-TRM phase 1. Materials and Structures/Materiaux et Constructions, 51(6), 1–14. https://doi.org/10.1617/s11527-018-1269-x10.1617/s11527-018-1269-x ↗
28. Liu, S., Zhang, T., Guo, Y., Wei, J., & Yu, Q. (2018). Effects of SCMs particles on the compressive strength of micro-structurally designed cement paste: Inherent characteristic effect, particle size refinement effect, and hydration effect. Powder Technology, 330, 1–11. https://doi.org/10.1016/j.powtec.2018.01.08710.1016/j.powtec.2018.01.087 ↗
29. Lothenbach, B., Scrivener, K., & Hooton, R. D. (2011). Supplementary cementitious materials. Cement and Concrete Research, 41(12), 1244–1256. https://doi.org/10.1016/j.cemconres.2010.12.00110.1016/j.cemconres.2010.12.001 ↗
30. Ma, M., Zhao, G., He, B., Li, Q., Dong, H., Wang, S., & Wang, Z. (2021). XGBoost-based method for flash flood risk assessment. Journal of Hydrology, 598(April), 126382. https://doi.org/10.1016/j.jhydrol.2021.12638210.1016/j.jhydrol.2021.126382 ↗
31. Merrick, L., & Taly, A. (2020). The explanation game: Explaining machine learning models using Shapley values. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (LNCS)Machine learning and knowledge extraction. (Vol. 12279). Springer International Publishing. https://doi.org/10.1007/978-3-030-57321-8_210.1007/978-3-030-57321-8_2 ↗
32. Mirzahosseini, M., & Riding, K. A. (2015). Influence of different particle sizes on reactivity of finely ground glass as supplementary cementitious material (SCM). Cement and Concrete Composites, 56, 95–105. https://doi.org/10.1016/j.cemconcomp.2014.10.00410.1016/j.cemconcomp.2014.10.004 ↗
33. Navarrete, I., Kurama, Y., Escalona, N., & Lopez, M. (2020). Impact of physical and physicochemical properties of supplementary cementitious materials on structural build-up of cement-based pastes. Cement and Concrete Research, 130(February), 105994. https://doi.org/10.1016/j.cemconres.2020.10599410.1016/j.cemconres.2020.105994 ↗
34. Ndahirwa, D., Zmamou, H., Lenormand, H., & Leblanc, N. (2022). The role of supplementary cementitious materials in hydration, durability and shrinkage of cement-based materials, their environmental and economic benefits: A review. Cleaner Materials, 5(June), 100123. https://doi.org/10.1016/j.clema.2022.10012310.1016/j.clema.2022.100123 ↗
35. Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-156510.1038/nbt1206-1565 ↗
36. Pacewska, B., & Wilińska, I. (2020). Usage of supplementary cementitious materials: Advantages and limitations: Part I. C–S–H, C–A–S–H and other products formed in different binding mixtures. Journal of Thermal Analysis and Calorimetry, 142(1), 371–393. https://doi.org/10.1007/s10973-020-09907-110.1007/s10973-020-09907-1 ↗
37. Pal, M., & Mather, P. M. (2001). Decision tree based classification of remotely sensed data. In Asian conference on remote sensing.10.1201/b12554 ↗
38. Patil, M. N., & Dubey, S. K. D. (2023). Effect of exposure condition, free water–cement ratio on quantities, rheological and mechanical properties of concrete Lecture notes in civil engineering (Vol. 260). Springer Nature. https://doi.org/10.1007/978-981-19-2145-2_1410.1007/978-981-19-2145-2_14 ↗
39. Pfingsten, J., Rickert, J., & Lipus, K. (2018). Estimation of the content of ground granulated blast furnace slag and different pozzolanas in hardened concrete. Construction and Building Materials, 165, 931–938. https://doi.org/10.1016/j.conbuildmat.2018.01.06510.1016/j.conbuildmat.2018.01.065 ↗
40. Phung, Q. T., Ferreira, E., Seetharam, S., Nguyen, V. T., Govaerts, J., & Valcke, E. (2021). Understanding hydration heat of mortars containing supplementary cementitious materials with potential to immobilize heavy metal containing waste. Cement and Concrete Composites, 115(May 2020), 103859. https://doi.org/10.1016/j.cemconcomp.2020.10385910.1016/j.cemconcomp.2020.103859 ↗
41. Rahla, K. M., Mateus, R., & Bragança, L. (2019). Comparative sustainability assessment of binary blended concretes using supplementary cementitious materials (SCMs) and ordinary Portland cement (OPC). Journal of Cleaner Production, 220, 445–459. https://doi.org/10.1016/j.jclepro.2019.02.01010.1016/j.jclepro.2019.02.010 ↗
42. Ramanathan, S., Pestana, L. R., & Suraneni, P. (2022). Reaction kinetics of supplementary cementitious materials in reactivity tests. Cement, 8(October 2021), 100022. https://doi.org/10.1016/j.cement.2022.10002210.1016/j.cement.2022.100022 ↗
43. Rasmussen, C. E. (2003). Gaussian processes in machine learning. Advanced lectures on machine learning (Vol. 3176/2004, pp. 63–71). Springer.
44. Reddy, V. M., & Rao, D. M. V. S. (2014). Effect of W/C ratio on workability and mechanical properties of high strength self compacting concrete (M70 grade). IOSR Journal of Mechanical and Civil Engineering, 11(5), 15–21. https://doi.org/10.9790/1684-1156152110.9790/1684-11561521 ↗
45. Sabir, B., Wild, S., & Bai, J. (2001). Metakaolin and calcined clays as pozzolans for concrete: A review. Cement and Concrete Composites, 23(6), 441–454. https://doi.org/10.1016/S0958-9465(00)00092-510.1016/S0958-9465(00)00092-5 ↗
46. Samad, S., & Shah, A. (2017). Role of binary cement including supplementary cementitious material (SCM), in production of environmentally sustainable concrete: A critical review. International Journal of Sustainable Built Environment, 6(2), 663–674. https://doi.org/10.1016/j.ijsbe.2017.07.00310.1016/j.ijsbe.2017.07.003 ↗
47. Sanjuán, M. Á., Argiz, C., Gálvez, J. C., & Moragues, A. (2015). Effect of silica fume fineness on the improvement of Portland cement strength performance. Construction and Building Materials, 96, 55–64. https://doi.org/10.1016/j.conbuildmat.2015.07.09210.1016/j.conbuildmat.2015.07.092 ↗
48. Schöler, A., Lothenbach, B., Winnefeld, F., Haha, M. B., Zajac, M., & Ludwig, H. M. (2017). Early hydration of SCM-blended Portland cements: A pore solution and isothermal calorimetry study. Cement and Concrete Research, 93, 71–82. https://doi.org/10.1016/j.cemconres.2016.11.01310.1016/j.cemconres.2016.11.013 ↗
49. Scrivener, K. L., Lothenbach, B., De Belie, N., Gruyaert, E., Skibsted, J., Snellings, R., & Vollpracht, A. (2015). TC 238-SCM: Hydration and microstructure of concrete with SCMs: State of the art on methods to determine degree of reaction of SCMs. Materials and Structures/Materiaux et Constructions, 48(4), 835–862. https://doi.org/10.1617/s11527-015-0527-410.1617/s11527-015-0527-4 ↗
50. Shi, J. Q., & Choi, T. (2011). Gaussian process regression analysis for functional data (1st ed.). CRC Press.10.1201/b11038 ↗
51. Simonsen, A. M. T., Solismaa, S., Hansen, H. K., & Jensen, P. E. (2020). Evaluation of mine tailings’ potential as supplementary cementitious materials based on chemical, mineralogical and physical characteristics. Waste Management, 102, 710–721. https://doi.org/10.1016/j.wasman.2019.11.03710.1016/j.wasman.2019.11.037 ↗
52. Skibsted, J., & Snellings, R. (2019). Reactivity of supplementary cementitious materials (SCMs) in cement blends. Cement and Concrete Research, 124(June), 105799. https://doi.org/10.1016/j.cemconres.2019.10579910.1016/j.cemconres.2019.105799 ↗
53. Snellings, R., Machner, A., Bolte, G., Kamyab, H., Durdzinski, P., Teck, P., et al. (2022). Hydration kinetics of ternary slag-limestone cements: Impact of water to binder ratio and curing temperature. Cement and Concrete Research, 151(July 2021), 106647. https://doi.org/10.1016/j.cemconres.2021.10664710.1016/j.cemconres.2021.106647 ↗
54. Snellings, R., & Scrivener, K. L. (2016). Rapid screening tests for supplementary cementitious materials: Past and future. Materials and Structures/Materiaux et Constructions, 49(8), 3265–3279. https://doi.org/10.1617/s11527-015-0718-z10.1617/s11527-015-0718-z ↗
55. Snoeck, D., Schaubroeck, D., Dubruel, P., & De Belie, N. (2014). Effect of high amounts of superabsorbent polymers and additional water on the workability, microstructure and strength of mortars with a water-to-cement ratio of 0.50. Construction and Building Materials, 72, 148–157. https://doi.org/10.1016/j.conbuildmat.2014.09.01210.1016/j.conbuildmat.2014.09.012 ↗
56. Song, Y. Y., & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.21504410.11919/j.issn.1002-0829.215044 ↗
57. Suraneni, P., Hajibabaee, A., Ramanathan, S., Wang, Y., & Weiss, J. (2019). New insights from reactivity testing of supplementary cementitious materials. Cement and Concrete Composites, 103(May), 331–338. https://doi.org/10.1016/j.cemconcomp.2019.05.01710.1016/j.cemconcomp.2019.05.017 ↗
58. Tironi, A., Trezza, M. A., Scian, A. N., & Irassar, E. F. (2013). Assessment of pozzolanic activity of different calcined clays. Cement and Concrete Composites, 37(1), 319–327. https://doi.org/10.1016/j.cemconcomp.2013.01.00210.1016/j.cemconcomp.2013.01.002 ↗
59. Walkley, B., & Provis, J. L. (2019). Solid-state nuclear magnetic resonance spectroscopy of cements. Materials Today Advances, 1, 100007. https://doi.org/10.1016/j.mtadv.2019.10000710.1016/j.mtadv.2019.100007 ↗
60. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr03007910.3354/cr030079 ↗
61. Wong, H. S., Poole, A. B., Wells, B., Eden, M., Barnes, R., Ferrari, J., et al. (2020). Microscopy techniques for determining water–cement (W/C) ratio in hardened concrete: A round-robin assessment. Materials and Structures/Materiaux et Constructions, 53(2), 1–19. https://doi.org/10.1617/s11527-020-1458-210.1617/s11527-020-1458-2 ↗
62. Zhang, D. (2017). A coefficient of determination for generalized linear models. American Statistician, 71(4), 310–316. https://doi.org/10.1080/00031305.2016.125683910.1080/00031305.2016.1256839 ↗