Please use this identifier to cite or link to this item: https://doi.org/10.3390/ma14092401
Title: Life-cycle assessment of alkali-activated materials incorporating industrial byproducts
Authors: Faridmehr, Iman
Nehdi, Moncef L.
Nikoo, Mehdi
Huseien, Ghasan Fahim 
Ozbakkaloglu, Togay
Keywords: Artificial neural network
CO2 emissions
Embodied energy
Industrial byproduct
Life cycle inventory
Sustainability
Issue Date: 5-May-2021
Publisher: MDPI AG
Citation: Faridmehr, Iman, Nehdi, Moncef L., Nikoo, Mehdi, Huseien, Ghasan Fahim, Ozbakkaloglu, Togay (2021-05-05). Life-cycle assessment of alkali-activated materials incorporating industrial byproducts. Materials 14 (9) : 2401. ScholarBank@NUS Repository. https://doi.org/10.3390/ma14092401
Rights: Attribution 4.0 International
Abstract: Eco-friendly and sustainable materials that are cost-effective, while having a reduced carbon footprint and energy consumption, are in great demand by the construction industry world-wide. Accordingly, alkali-activated materials (AAM) composed primarily of industrial byproducts have emerged as more desirable alternatives to ordinary Portland cement (OPC)-based concrete. Hence, this study investigates the cradle-to-gate life-cycle assessment (LCA) of ternary blended al-kali-activated mortars made with industrial byproducts. Moreover, the embodied energy (EE), which represents an important parameter in cradle-to-gate life-cycle analysis, was investigated for 42 AAM mixtures. The boundary of the cradle-to-gate system was extended to include the mechanical and durability properties of AAMs on the basis of performance criteria. Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs. Considering the lack of systematic research on the cradle-to-gate LCA of AAMs in the liter-ature, the results of this research provide new insights into the assessment of the environmental impact of AAM made with industrial byproducts. The final weight and bias values of the AAN model can be used to design AAM mixtures with targeted mechanical properties and CO2 emission considering desired amounts of industrial byproduct utilization in the mixture. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Materials
URI: https://scholarbank.nus.edu.sg/handle/10635/233260
ISSN: 1996-1944
DOI: 10.3390/ma14092401
Rights: Attribution 4.0 International
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