Comparative assessment of multiple machine learning models for predicting the compressive strength of multiwalled carbon nanotube reinforced high volume fly ash concrete
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Concrete and Cement Materials Research · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (57)
Show source-provided references
1. Makul N (2020) Advanced smart concrete – A review of current progress, benefits and challenges. J Clean Prod 274:122899. https://doi.org/10.1016/j.jclepro.2020.12289910.1016/j.jclepro.2020.122899 ↗
2. Mikulčić H, Klemeš JJ, Vujanović M, Urbaniec K, Duić N (2016) Reducing greenhouse gasses emissions by fostering the deployment of alternative Raw materials and energy sources in the cleaner cement manufacturing process. J Clean Prod 136:119–132. https://doi.org/10.1016/j.jclepro.2016.04.14510.1016/j.jclepro.2016.04.145 ↗
3. Naik TR (2020) Sustainability of the cement and concrete industries. Sustainable Constr Mater Technol 19–25. https://doi.org/10.1201/978100306102110.1201/9781003061021 ↗
4. Li C, Gong XZ, Cui SP, Wang ZH, Zheng Y, Chi BC (2011) CO₂ emissions due to cement manufacture. Mater Sci Forum 685:181–187. https://doi.org/10.4028/www.scientific.net/MSF.685.18110.4028/www.scientific.net/MSF.685.181 ↗
5. Worrell E, Price L, Martin N, Hendriks C, Meida LO (2001) Carbon dioxide emissions from the global cement industry. Annu Rev Energy Environ 26(1):303–329. https://doi.org/10.1146/annurev.energy.26.1.30310.1146/annurev.energy.26.1.303 ↗
6. Ahmed Ali K, Ahmad MI, Yusup Y (2020) Issues, impacts, and mitigations of carbon dioxide emissions in the Building sector. Sustainability 12(18):7427. https://doi.org/10.3390/su1218742710.3390/su12187427 ↗
7. Nielsen CV (2008) Carbon footprint of concrete buildings seen in the life cycle perspective. Proceedings NRMCA Concrete Technology Forum, 1–14
8. Akan MÖA, Dhavale DG, Sarkis J (2017) Greenhouse gas emissions in the construction industry: an analysis and evaluation of a concrete supply chain. J Clean Prod 167:1195–1207. https://doi.org/10.1016/j.jclepro.2017.07.22510.1016/j.jclepro.2017.07.225 ↗
9. Habert G, Miller SA, John VM, Provis JL, Favier A, Horvath A, Scrivener KL (2020) Environmental impacts and decarbonization strategies in the cement and concrete industries. Nat Reviews Earth Environ 1(11):559–573. https://doi.org/10.1038/s43017-020-0093-310.1038/s43017-020-0093-3 ↗
10. Barcelo L, Kline J, Walenta G, Gartner E (2014) Cement and carbon emissions. Mater Struct 47(6):1055–1065. https://doi.org/10.1617/s11527-013-0114-510.1617/s11527-013-0114-5 ↗
11. Wong JKW, Zhou J (2015) Enhancing environmental sustainability over Building life cycles through green BIM: A review. Autom Constr 57:156–165. https://doi.org/10.1016/j.autcon.2015.06.00310.1016/j.autcon.2015.06.003 ↗
12. Ribeiro J, Silva TF, Mendonça Filho JG, Flores D (2014) Fly Ash from coal combustion – An environmental source of organic compounds. Appl Geochem 44:103–110. https://doi.org/10.1016/j.apgeochem.2013.06.01410.1016/j.apgeochem.2013.06.014 ↗
13. Vig N, Mor S, Ravindra K (2023) The multiple value characteristics of fly Ash from Indian coal thermal power plants: A review. Environ Monit Assess 195(1):33. https://doi.org/10.1007/s10661-022-10473-210.1007/s10661-022-10473-2 ↗
14. Page AL, Elseewi AA (1979) Straughan I R. Physical and chemical properties of fly ash from coal-fired power plants with reference to environmental impacts. Residue Reviews: Residues of Pesticides and Other Contaminants in the Total Environment, 83–120. https://doi.org/10.1007/978-1-4612-6185-8_210.1007/978-1-4612-6185-8_2 ↗
15. Vairagade VS, Parbat DK, Dhale SA (2015) Fly Ash as sustainable material for green concrete – A state of Art. Int J Res Eng Sci Technol 1(2):17–24
16. Limbachiya M, Meddah MS, Ouchagour Y (2012) Use of recycled concrete aggregate in fly-ash concrete. Constr Build Mater 27(1):439–44910.1016/j.conbuildmat.2011.07.023 ↗
17. Vargas J, Halog A (2015) Effective carbon emission reductions from using upgraded fly Ash in the cement industry. J Clean Prod 103:948–959. https://doi.org/10.1016/j.jclepro.2015.04.13610.1016/j.jclepro.2015.04.136 ↗
18. Runganga D, Okonta F, Musonda I (2024) Strength and durability properties of high-volume fly Ash (HVFA) binders: A systematic review. CivilEng 5(2):435–460. https://doi.org/10.3390/civileng502002210.3390/civileng5020022 ↗
19. Teixeira ER (2019) High volume coal and biomass fly ash eco-efficient concrete. Doctoral Dissertation, Universidade do Minho (Portugal)
20. Talaat A, Emad A, Tarek A, Masbouba M, Essam A, Kohail M (2021) Factors affecting the results of concrete compression testing: A review. Ain Shams Eng J 12(1):205–221. https://doi.org/10.1016/j.asej.2020.07.01510.1016/j.asej.2020.07.015 ↗
21. Wang R, Huo Y, Wang T, Hou P, Gong Z, Li G, Li C (2024) Machine learning method to explore the correlation between fly Ash content and chloride resistance. Materials 17(5):1192. https://doi.org/10.3390/ma1705119210.3390/ma17051192 ↗
22. Kumar A, Sinha S (2024) Role of multiwalled carbon nanotube in the improvement of compaction and strength characteristics of fly Ash stabilized soil. Int J Pavement Res Technol 17(4):868–889. https://doi.org/10.1007/s42947-023-00274-210.1007/s42947-023-00274-2 ↗
23. Kumar A, Sinha S (2024) Support vector machine-based prediction of unconfined compressive strength of multi-walled carbon nanotube doped soil-fly Ash mixes. Multiscale Multidisciplinary Model Experiments Des 2024:1–22. https://doi.org/10.1007/s41939-024-00524-110.1007/s41939-024-00524-1 ↗
24. Kumar A, Sinha S, Saurav S (2024) Random Forest, CART, and MLR-based predictive model for unconfined compressive strength of cement reinforced clayey soil: A comparative analysis. Asian J Civil Eng 25(2):2307–2323. https://doi.org/10.1007/s42107-023-00909-610.1007/s42107-023-00909-6 ↗
25. Kumar A, Sinha S, Srivastava PK (2024) Stochastic investigation into the heterogeneity of multiwalled carbon nanotube infused clay bricks. J Mater Civ Eng 36(4):04024046. https://doi.org/10.1061/JMCEE7.MTENG-1712810.1061/JMCEE7.MTENG-17128 ↗
26. Kumar A, Sinha S, Pandey D, Maurya MC, Chauhan VB (2024) Advanced regression models for assessing the strength of multi-walled carbon nanotube-modified high-volume fly Ash concrete. Asian J Civil Eng 25(2):2247–2268. https://doi.org/10.1007/s42107-023-00906-910.1007/s42107-023-00906-9 ↗
27. Kumar A, Sinha S, Saurav S, Chauhan VB (2024) Prediction of unconfined compressive strength of cement–fly Ash stabilized soil using support vector machines. Asian J Civil Eng 25(2):1149–1161. https://doi.org/10.1007/s42107-023-00833-910.1007/s42107-023-00833-9 ↗
28. Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125. https://doi.org/10.1016/j.advengsoft.2017.09.00410.1016/j.advengsoft.2017.09.004 ↗
29. Sathvik S, Kumar R, Ulloa N, Shakor P, Ujwal MS, Onyelowe K, Kumar GS, Christo MS (2024) Modelling the mechanical properties of concrete produced with polycarbonate waste Ash by machine learning. Sci Rep 14(1):11552. https://doi.org/10.1038/s41598-024-62412-510.1038/s41598-024-62412-5 ↗
30. Manan A, Zhang P, Ahmad S, Umar M, Raza A (2024) Machine learning prediction model integrating experimental study for compressive strength of carbon-nanotubes composites. J Eng Res. https://doi.org/10.1016/j.jer.2024.08.00710.1016/j.jer.2024.08.007 ↗
31. Tipu RK, Arora R, Kumar K (2024) Machine learning-based prediction of concrete strength properties with coconut shell as partial aggregate replacement: A sustainable approach in construction engineering. Asian J Civil Eng 25(3):2979–2992. https://doi.org/10.1007/s42107-023-00957-y10.1007/s42107-023-00957-y ↗
32. Lamba P, Kaur DP, Raj S, Tipu RK, Sorout J, Malik A, Khan AA (2024) Repurposing plastic waste: experimental study and predictive analysis using machine learning in bricks. J Mol Struct 1317:139158. https://doi.org/10.1016/j.molstruc.2024.13915810.1016/j.molstruc.2024.139158 ↗
33. Sun L, Wang X, Zhang C (2024), December Three-dimensional high fidelity mesoscale rapid modelling algorithm for concrete. In Structures (Vol. 70, p. 107561). Elsevier. https://doi.org/10.1016/j.istruc.2024.10756110.1016/j.istruc.2024.107561 ↗
34. Yang G, Zhao H, Hu Z, Zhang W, Xiang Y, Jin M, Liu J (2025) Prediction of restrained stress for UHPC: considering relationship between long-term and in-situ creep. Constr Build Mater 484:141722. https://doi.org/10.1016/j.conbuildmat.2025.14172210.1016/j.conbuildmat.2025.141722 ↗
35. Liu Y, Han Y, Shi T, Lv B, Zhang F, Zhou X (2025) An innovative method for Preparing nano steel slag powder and analysis of carbonation reaction mechanism toward enhanced CO2 sequestration. Mater Lett. https://doi.org/10.1016/j.matlet.2025.13941110.1016/j.matlet.2025.139411 ↗
36. Song Y, Xu R, Hong J, Zhou Y, Shi T, Wang C, Xie C (2025) Preparation of a novel green lithium slag expansive agent: enhancing deformation control, crack resistance, and durability of mortar. Constr Build Mater 495:143644. https://doi.org/10.1016/j.conbuildmat.2025.14364410.1016/j.conbuildmat.2025.143644 ↗
37. Wei X, Xie S, Luo A, Liu Y, Li Y, Wu L (2025) Performance improvement of ecologically friendly ultra-high performance concrete with waste tire recycled steel fiber. Constr Build Mater 495:143607. https://doi.org/10.1016/j.conbuildmat.2025.14360710.1016/j.conbuildmat.2025.143607 ↗
38. Long X, Mao MH, Su TX, Su YT, Tian MK (2023) Machine learning method to predict dynamic compressive response of concrete-like material at high strain rates. Def Technol 23:100–111. https://doi.org/10.1016/j.dt.2022.02.00310.1016/j.dt.2022.02.003 ↗
39. Uddin MA, Sobuz MHR, Kabbo MK, I., Tilak MKC, Lal R, Reza MS, Abdullah M (2025) Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses. Sci Rep 15(1):26330. https://doi.org/10.1038/s41598-025-11601-x10.1038/s41598-025-11601-x ↗
40. Lu D, Meng F, Zhou X, Zhuo Y, Gao Z, Du X (2023) A dynamic elastoplastic model of concrete based on a modeling method with environmental factors as constitutive variables. J Eng Mech 149(12):04023102. https://doi.org/10.1061/JENMDT.EMENG-720610.1061/JENMDT.EMENG-7206 ↗
41. Sobuz MHR, Tilak MKC, Rahman SA, Aditto FS, Shaikh FUA, Kabbo MKI, Khan MMH (2025) Comparative performance evaluation of quartz and snail shell powders modified concrete: Mechanical, machine learning, and microstructural assessments. J Mater Res Technol 36:7766–7782. https://doi.org/10.1016/j.jmrt.2025.04.29610.1016/j.jmrt.2025.04.296 ↗
42. Zhou X, Lu D, Zhao J, Zhang Y, Gao Z, Rabczuk T, Du X (2025) Material characteristic length insensitive nonlocal modelling: A computationally efficient scaled nonlocal integral method. Comput Geotech 188:107587. https://doi.org/10.1016/j.compgeo.2025.10758710.1016/j.compgeo.2025.107587 ↗
43. Wang K, Chen Z, Wang Z, Chen Q, Ma D (2023) Critical dynamic stress and cumulative plastic deformation of calcareous sand filler based on shakedown theory. J Mar Sci Eng 11(1):195. https://doi.org/10.3390/jmse1101019510.3390/jmse11010195 ↗
44. Hasan NMS, Murshed NM, Noman A, Miah MA, Sobuz MJ, M. H. R., Kabbo MKI (2025) Performance of fiber reinforced low-carbon concrete: role of corn cob Ash and corn husk fiber. J Sustain Cement-Based Mater 1–21. https://doi.org/10.1080/21650373.2025.258042410.1080/21650373.2025.2580424 ↗
45. Yaprak H, Karacı A, Demir I (2013) Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Comput Appl 22:133–141. https://doi.org/10.1007/s00521-011-0671-x10.1007/s00521-011-0671-x ↗
46. Adamu M, Çolak AB, Ibrahim YE, Haruna SI, Hamza MF (2023) Prediction of mechanical properties of rubberized concrete incorporating fly Ash and nano silica by artificial neural network technique. Axioms 12(1):81. https://doi.org/10.3390/axioms1201008110.3390/axioms12010081 ↗
47. Abd AM, Abd SM (2017) Modelling the strength of lightweight foamed concrete using support vector machine (SVM). Case Stud Constr Mater 6:8–15. https://doi.org/10.1016/j.cscm.2016.11.00210.1016/j.cscm.2016.11.002 ↗
48. Shah SFA, Chen B, Zahid M, Ahmad MR (2022) Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning. Constr Build Mater 360:129534. https://doi.org/10.1016/j.conbuildmat.2022.12953410.1016/j.conbuildmat.2022.129534 ↗
49. Mohammed A, Burhan L, Ghafor K, Sarwar W, Mahmood W (2021) Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers. Neural Comput Appl 33(13):7851–7873. https://link.springer.com/https://doi.org/10.1007/s00521-020-05525-y10.1007/s00521-020-05525-y ↗
50. Piro NS, Salih A, Hamad SM, Kurda R (2021) Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions. J Mater Res Technol 15:6506–6527. https://doi.org/10.1016/j.jmrt.2021.11.02810.1016/j.jmrt.2021.11.028 ↗
51. Iqbal M, Zhang D, Jalal FE, Javed MF (2021) Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment. Ocean Eng 232:109134. https://doi.org/10.1016/j.oceaneng.2021.10913410.1016/j.oceaneng.2021.109134 ↗
52. Kumar B, Sinha S, Chakravarty H (2019) Study of effect of nano-silica on strength and durability characteristics of high-volume fly Ash concrete for pavement construction. Civil Eng J 1341–1352. https://doi.org/10.28991/cej-2019-0309133610.28991/cej-2019-03091336 ↗
53. IS 10262 (2009) Concrete mix proportioning – Guidelines, 1st edn. Bureau of Indian Standards
54. IRC 44 (2017) Guidelines for cement concrete mix design for pavements, 4th edn. Indian Roads Congress
55. IS 516 (1959) Method of tests for strength of concrete. Bureau of Indian Standards
56. Tabarsa A, Latifi N, Osouli A, Bagheri Y (2021) Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines. Front Struct Civil Eng 15(2):520–536. https://doi.org/10.1007/s11709-021-0689-910.1007/s11709-021-0689-9 ↗
57. Singh R, Tipu RK, Mir AA, Patel M (2025) Predictive modelling of flexural strength in recycled aggregate-based concrete: A comprehensive approach with machine learning and global sensitivity analysis. Iran J Sci Technol Trans Civil Eng 49(2):1089–1114. https://doi.org/10.1007/s40996-024-01502-w10.1007/s40996-024-01502-w ↗