Please use this identifier to cite or link to this item: https://doi.org/10.3390/su13042062
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dc.titleAssessment of mechanical properties and structural morphology of alkali-activated mortars with industrial waste materials
dc.contributor.authorFaridmehr, Iman
dc.contributor.authorBedon, Chiara
dc.contributor.authorHuseien, Ghasan Fahim
dc.contributor.authorNikoo, Mehdi
dc.contributor.authorBaghban, Mohammad Hajmohammadian
dc.date.accessioned2022-10-11T08:02:59Z
dc.date.available2022-10-11T08:02:59Z
dc.date.issued2021-02-14
dc.identifier.citationFaridmehr, Iman, Bedon, Chiara, Huseien, Ghasan Fahim, Nikoo, Mehdi, Baghban, Mohammad Hajmohammadian (2021-02-14). Assessment of mechanical properties and structural morphology of alkali-activated mortars with industrial waste materials. Sustainability (Switzerland) 13 (4) : Jan-25. ScholarBank@NUS Repository. https://doi.org/10.3390/su13042062
dc.identifier.issn2071-1050
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232140
dc.description.abstractAlkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al2O3, SiO2, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption. © 2021 by the authors.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectAlkali-activated mix design
dc.subjectCO2 emission
dc.subjectCompressive strength
dc.subjectEmbodied energy
dc.subjectFly ash
dc.subjectGranulated blast-furnace slag
dc.subjectGreen mortar
dc.subjectOrdinary portland cement
dc.subjectPalm oil fly ash
dc.subjectRecycled ceramics
dc.typeArticle
dc.contributor.departmentDEPT OF THE BUILT ENVIRONMENT
dc.description.doi10.3390/su13042062
dc.description.sourcetitleSustainability (Switzerland)
dc.description.volume13
dc.description.issue4
dc.description.pageJan-25
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