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Cement kiln dust (CKD) is a fine powder similar to Portland cement in appearance and produced during grinding and burning of the raw material in the cement kiln. Large amounts of CKD have accumulated in the cement factories annually. It is necessary to re-use this waste material in the construction field instead of landfilling, causing environmental problems, such as the death of vegetation and groundwater pollution. One of the choices to re-use CKD is replacing Portland cement in cement-based mortar and reducing greenhouse gas emissions like carbon dioxide to the atmosphere. This study evaluated the effect of the main two components of CKD, such as SiO2 and CaO, on the long-term compressive strength of cement-based mortar up to 360 days of curing. For that purpose, 167 data of cement-based mortar samples modified with CKD were collected from literature and analyzed. Water-to-binder ratio (w/b) was ranged from 0.34 to 0.76, CKD content ranged from 0 to 50% (dry weight of cement), different CaO and SiO2 of CKD and cement are ranged from 17.64 to 25.45%, and 51.45 to 65.57%, respectively. Several soft computing models, such as multi-expression programming (MEP), artificial neural network (ANN), nonlinear regression (NLR), and full quadratic (FQ), were developed to predict the compressive strength of the cement mortar modified with CKD. Statistical assessment tools were also used to evaluate the proposed models. It was obtained from the modeling results that are increasing SiO2(%) increased the compressive strength of the mortar, and increasing CaO (%) decreased compressive strength for CKD content from 0 to 15% and increasing compressive strength for CKD content of 15-50%. According to the assessment criteria, the ANN model predicted compressive strength up to 360 days of curing better than other developed models with high R2 and a20-index and low RMSE, MAE, SI, and OBJ. The second-best model was the MEP model. Based on the sensitivity analysis, the curing time is the most influential parameter in compressive strength prediction of cement-based mortar modified with CKD.
Czasopismo
Rocznik
Tom
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art. no. e105
Opis fizyczny
Bibliogr. 46 poz., rys., tab., wykr.
Twórcy
autor
- College of Engineering, Civil Engineering Department, University of Sulaimani, Kurdistan‑Iraq, Kirkuk Rd, Sulaymaniyah 46001, Iraq
autor
- College of Engineering, Civil Engineering Department, University of Sulaimani, Kurdistan‑Iraq, Kirkuk Rd, Sulaymaniyah 46001, Iraq
Bibliografia
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- 18. Vipulanandan C, Mohammed A. Magnetic field strength and temperature effects on the behavior of oil well cement slurry modified with iron oxide nanoparticles and quantified with vipulanandan models. J Test Eval. 2019;48(6):4516-37.
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- 27. Huat CY, Moosavi SMH, Mohammed AS, Armaghani DJ, Ulrikh DV, Monjezi M, Hin Lai S. Factors influencing pile friction bearing capacity: proposing a novel procedure based on gradient boosted tree technique. Sustainability. 2021;13(21):11862.
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- 40. Najim KB, Mahmod ZS, Atea A-KM. Experimental investigation on using cement kiln dust (CKD) as a cement replacement material in producing modified cement mortar. Constr Build Mater. 2014;55:5-12. https://doi.org/10.1016/j.conbuildmat.2014.01.015.
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- 43. Kassim MM. The durability of sulfate resisting mortar with partial replacement by cement kiln dust. Anti-Corros Methods Mater. 2014. https://doi.org/10.1108/ACMM-02-2013-1243.
- 44. Mohammed A, Hummadi RA, Mawlood YI. Predicting the chemical and mechanical properties of gypseous soils using different simulation technics. Acta Geotech. 2021;. https://doi.org/10.1007/s11440-021-01304-8.
- 45. Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG. Soft computing-based techniques for concrete beams shear strength. Procedia Struct Integr. 2019;17:924-33. https://doi.org/10.1016/j.prostr.2019.08. 123.
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Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d7eca3da-8550-4026-9b06-72649c8c5d4a