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Cited by 73Concrete and Cement Materials ResearchEnvironmental Impact and SustainabilityMaterials Engineering and Processing

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Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts

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Faridmehr, Iman, Nehdi, Moncef L., Nikoo, Mehdi, Huseien, Ghasan Fahim, Ozbakkaloglu, Togay·2021·Materials
DOI 10.3390/ma14092401PMC PMC8125601
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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 worldwide. 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 alkali-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 literature, 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.

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Figures

Figure 1 Production stage of alkali-activated materials (AAMs).
Figure 1 Production stage of alkali-activated materials (AAMs).CC BY · PubMed Central
Figure 2 Cradle-to-gate and modified LCA boundary.
Figure 2 Cradle-to-gate and modified LCA boundary.CC BY · PubMed Central
Figure 3 Effects of exposure to sulfuric acid solution on CS and mass loss of AAM mortars.
Figure 3 Effects of exposure to sulfuric acid solution on CS and mass loss of AAM mortars.CC BY · PubMed Central
Figure 4 Effects of exposure to sulphate solution on CS and weight of AAM mortars.
Figure 4 Effects of exposure to sulphate solution on CS and weight of AAM mortars.CC BY · PubMed Central
Figure 5 Physical appearance of cubic AAM mortar specimens made with different industrial byproducts: (a) control sample, (b) after 365-days of immersion in sulfuric acid solution, and (c) after 365-days of immersion in sulphate solution.
Figure 5 Physical appearance of cubic AAM mortar specimens made with different industrial byproducts: (a) control sample, (b) after 365-days of immersion in sulfuric acid solution, and (c) after 365-days of immersion in sulphate solution.CC BY · PubMed Central
Figure 6 Relationship between ultrasonic pulse velocity (UPV) and CS for all 42 AAM mixtures: (a) original condition, (b) after immersion in sulfuric acid solution, and (c) after immersion in sulphate solution.
Figure 6 Relationship between ultrasonic pulse velocity (UPV) and CS for all 42 AAM mixtures: (a) original condition, (b) after immersion in sulfuric acid solution, and (c) after immersion in sulphate solution.CC BY · PubMed Central
Figure 7 Distribution of CO2 emission by AAMs ingredient and phase.
Figure 7 Distribution of CO2 emission by AAMs ingredient and phase.CC BY · PubMed Central
Figure 8 Distribution of EE by AAMs ingredient and phase.
Figure 8 Distribution of EE by AAMs ingredient and phase.CC BY · PubMed Central
Figure 9 Illustration of compressive strength versus normalized CO2 emissions.
Figure 9 Illustration of compressive strength versus normalized CO2 emissions.CC BY · PubMed Central
Figure 10 UPV versus normalized CO2 emission subjected to sulfuric acid attack.
Figure 10 UPV versus normalized CO2 emission subjected to sulfuric acid attack.CC BY · PubMed Central
Figure 11 UPV versus normalized CO2 emission subjected to sulphate attack. Add unit for UPV.
Figure 11 UPV versus normalized CO2 emission subjected to sulphate attack. Add unit for UPV.CC BY · PubMed Central
Figure 12 Scatter graph of the total density of input parameters using principal component analysis (PCA).
Figure 12 Scatter graph of the total density of input parameters using principal component analysis (PCA).CC BY · PubMed Central
Figure 13 Topology of a feed-forward artificial neural network (ANN) with two hidden layers (4–5–4–2 structure).
Figure 13 Topology of a feed-forward artificial neural network (ANN) with two hidden layers (4–5–4–2 structure).CC BY · PubMed Central
Figure 14 Probability plot diagrams for (a) CO2 emissions, and (b) EE.
Figure 14 Probability plot diagrams for (a) CO2 emissions, and (b) EE.CC BY · PubMed Central
Figure 15 Predicted vs. experimental values of EE estimated by the PCA-COA-ANN model.
Figure 15 Predicted vs. experimental values of EE estimated by the PCA-COA-ANN model.CC BY · PubMed Central
Figure 16 Predicted vs. experimental values of CO2 emissions estimated by the PCA-COA-ANN model.
Figure 16 Predicted vs. experimental values of CO2 emissions estimated by the PCA-COA-ANN model.CC BY · PubMed Central
Figure 17 Sensitivity indices of variable (a) CO2 emissions and (b) EE.
Figure 17 Sensitivity indices of variable (a) CO2 emissions and (b) EE.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 50

  1. Youn, 2019, Carbon dioxide sequestration process for the cement industry, J. CO2 Util., 34, 325, 10.1016/j.jcou.2019.07.023 · DOI
  2. Gupta, N., Siddique, R., and Belarbi, R. (2021). Sustainable and Greener Self-Compacting Concrete incorporating Industrial By-Products: A Review. J. Clean. Prod., 284. · DOI
  3. Hwang, 2020, Physical–microstructural evaluation and sulfate resistance of no-cement mortar developed from a ternary binder of industrial by-products, Environ. Prog. Sustain. Energy, 39, e13421, 10.1002/ep.13421 · DOI
  4. Hossain, M.U., Wang, L., Chen, L., Tsang, D.C.W., Ng, S.T., Poon, C.S., and Mechtcherine, V. (2020). Evaluating the environmental impacts of stabilization and solidification technologies for managing hazardous wastes through life cycle assessment: A case study of Hong Kong. Environ. Int., 145. · DOI
  5. Wi, S., Yang, S., Berardi, U., and Kim, S. (2019). Assessment of recycled ceramic-based inorganic insulation for improving energy efficiency and flame retardancy of buildings. Environ. Int., 130. · DOI
  6. Tucker, 2018, Economic and life cycle assessment of recycling municipal glass as a pozzolan in portland cement concrete production, Resour. Conserv. Recycl., 129, 240, 10.1016/j.resconrec.2017.10.025 · DOI
  7. Sinka, 2018, Comparative life cycle assessment of magnesium binders as an alternative for hemp concrete, Resour. Conserv. Recycl., 133, 288, 10.1016/j.resconrec.2018.02.024 · DOI
  8. Yasin, S., Behary, N., Curti, M., and Rovero, G. (2016). Global Consumption of Flame Retardants and Related Environmental Concerns: A Study on Possible Mechanical Recycling of Flame Retardant Textiles. Fibers, 4. · DOI
  9. Thomas, 2015, Alkali-activated concrete: Engineering properties and stress–strain behavior, Constr. Build. Mater., 93, 49, 10.1016/j.conbuildmat.2015.04.039 · DOI
  10. Ding, 2016, Mechanical properties of alkali-activated concrete: A state-of-the-art review, Constr. Build. Mater., 127, 68, 10.1016/j.conbuildmat.2016.09.121 · DOI
  11. Thomas, 2018, Comparison of chloride permeability methods for Alkali-Activated concrete, Constr. Build. Mater., 165, 104, 10.1016/j.conbuildmat.2018.01.016 · DOI
  12. Huseien, G.F., and Shah, K.W. (2020). Durability and life cycle evaluation of self-compacting concrete containing fly ash as GBFS replacement with alkali activation. Constr. Build. Mater., 235. · DOI
  13. Hanjitsuwan, 2016, Flexural strength of notched concrete beam filled with alkali-activated binders under different types of alkali solutions, Constr. Build. Mater., 127, 673, 10.1016/j.conbuildmat.2016.10.053 · DOI
  14. Paris, 2018, Alkali-activated grouts with incorporated fly ash: From NMR analysis to mechanical properties, Mater. Today Commun., 14, 225, 10.1016/j.mtcomm.2018.01.012 · DOI
  15. 2018, Life cycle assessment (LCA) of an alkali-activated binary concrete based on natural volcanic pozzolan: A comparative analysis to OPC concrete, Constr. Build. Mater., 176, 103, 10.1016/j.conbuildmat.2018.05.017 · DOI
  16. Bajpai, R., Choudhary, K., Srivastava, A., Sangwan, K.S., and Singh, M. (2020). Environmental impact assessment of fly ash and silica fume based geopolymer concrete. J. Clean. Prod., 254. · DOI
  17. Abdulkareem, M., Havukainen, J., and Horttanainen, M. (2019). How environmentally sustainable are fibre reinforced alkali-activated concretes?. J. Clean. Prod., 236. · DOI
  18. Nazer, 2016, Use of ancient copper slags in Portland cement and alkali activated cement matrices, J. Environ. Manag., 167, 115, 10.1016/j.jenvman.2015.11.024 · DOI
  19. Hou, P., Jolliet, O., Zhu, J., and Xu, M. (2020). Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models. Environ. Int., 135. · DOI
  20. Anastasiou, 2015, Comparative life cycle assessment of concrete road pavements using industrial by-products as alternative materials, Resour. Conserv. Recycl., 101, 1, 10.1016/j.resconrec.2015.05.009 · DOI
  21. Oyebisi, S., Ede, A., Olutoge, F., and Ngene, B. (2020). Assessment of activity indexes on the splitting tensile strengthening of geopolymer concrete incorporating supplementary cementitious materials. Mater. Today Commun., 24. · DOI
  22. Dhandapani, 2018, Mechanical properties and durability performance of concretes with Limestone Calcined Clay Cement (LC3), Cem. Concr. Res., 107, 136, 10.1016/j.cemconres.2018.02.005 · DOI
  23. Wang, 2019, Utilizing coral waste and metakaolin to produce eco-friendly marine mortar: Hydration, mechanical properties and durability, J. Clean. Prod., 219, 763, 10.1016/j.jclepro.2019.02.147 · DOI
  24. Li, 2016, Drying shrinkage prediction of paste containing meta-kaolin and ultrafine fly ash for developing ultra-high performance concrete, Mater. Today Commun., 6, 74, 10.1016/j.mtcomm.2016.01.001 · DOI
  25. Zhang, P., Dai, Y., Wang, W., Yang, J., Mo, L., Guo, W., and Bao, J. (2020). Effects of magnesia expansive agents on the self-healing performance of microcracks in strain-hardening cement-based composites (SHCC). Mater. Today Commun., 25. · DOI
  26. Ison, 2000, The use of lca to introduce life-cycle thinking into decision-making for the purchase of medical devices in the nhs, J. Environ. Assess. Policy Manag., 2, 453, 10.1142/S1464333200000497 · DOI · Paper page
  27. Hermann, 2007, Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators, J. Clean Prod., 15, 1787, 10.1016/j.jclepro.2006.04.004 · DOI
  28. Yasin, 2018, Life cycle assessment of flame retardant cotton textiles with optimized end-of-life phase, J. Clean. Prod., 172, 1080, 10.1016/j.jclepro.2017.10.198 · DOI
  29. Yasin, 2019, Propelling textile waste to ascend the ladder of sustainability: EOL study on probing environmental parity in technical textiles, J. Clean. Prod., 233, 1451, 10.1016/j.jclepro.2019.06.009 · DOI
  30. (2021, March 20). Book review: Embodied Carbon: The Inventory of Carbon and Energy (ICE). A BSRIA Guide. Available online: https://www.icevirtuallibrary.com/doi/pdf/10.1680/ener.2011.164.4.206. · DOI
  31. Hammond, 2008, Embodied energy and carbon in construction materials, Proc. Inst. Civ. Eng. Energy, 161, 87
  32. Knoeri, 2013, Comparative LCA of recycled and conventional concrete for structural applications, Int. J. Life Cycle Assess., 18, 909, 10.1007/s11367-012-0544-2 · DOI
  33. Teixeira, 2019, Quality and durability properties and life-cycle assessment of high volume biomass fly ash mortar, Constr. Build. Mater., 197, 195, 10.1016/j.conbuildmat.2018.11.173 · DOI
  34. Li, J., Zhang, W., Li, C., and Monteiro, P.J. (2020). Eco-friendly mortar with high-volume diatomite and fly ash: Performance and life-cycle assessment with regional variability. J. Clean. Prod., 261. · DOI
  35. AzariJafari, 2019, Ternary blended cement: An eco-friendly alternative to improve resistivity of high-performance self-consolidating concrete against elevated temperature, J. Clean. Prod., 223, 575, 10.1016/j.jclepro.2019.03.054 · DOI
  36. Gursel, 2016, A life-cycle approach to environmental, mechanical, and durability properties of “green” concrete mixes with rice husk ash, J. Clean. Prod., 112, 823, 10.1016/j.jclepro.2015.06.029 · DOI
  37. Lee, 2013, Setting and mechanical properties of alkali-activated fly ash/slag concrete manufactured at room temperature, Constr. Build. Mater., 47, 1201, 10.1016/j.conbuildmat.2013.05.107 · DOI
  38. Puertas, 2017, Alkali-activated Portland blast-furnace slag cement: Mechanical properties and hydration, Constr. Build. Mater., 140, 119, 10.1016/j.conbuildmat.2017.02.092 · DOI
  39. Song, X.J., Marosszeky, M., Brungs, M., and Mun, R. (2005, January 17–20). Durability of Fly Ash based Geopolymer Concrete against Sulphuric Acid Attack. Proceedings of the 10DBMC International Conference On Durability of Building Materials and Components, Lyon, France.
  40. Trtnik, 2009, Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks, Ultrason., 49, 53, 10.1016/j.ultras.2008.05.001 · DOI
  41. Jeon, 2020, Investigation of sulfuric acid attack upon cement mortars containing silicon carbide powder, Powder Technol., 359, 181, 10.1016/j.powtec.2019.10.026 · DOI
  42. García-Vera, V.E., Tenza-Abril, A.J., Saval, J.M., and Lanzón, M. (2018). Influence of Crystalline Admixtures on the Short-Term Behaviour of Mortars Exposed to Sulphuric Acid. Materials, 12. · DOI
  43. Nikoo, 2018, Determining the natural frequency of cantilever beams using ANN and heuristic search, Appl. Artif. Intell., 32, 309, 10.1080/08839514.2018.1448003 · DOI
  44. Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer.
  45. Rajabioun, 2011, Cuckoo Optimization Algorithm, Appl. Soft Comput., 11, 5508, 10.1016/j.asoc.2011.05.008 · DOI
  46. Jolliffe, I.T., and Jolliffe, I.T. (1986). Graphical Representation of Data Using Principal Components. Linear Gener. Linear Mixed Models Appl., 64–91. · DOI
  47. Hubbard, 1987, An empirical comparison of alternative methods for principal component extraction, J. Bus. Res., 15, 173, 10.1016/0148-2963(84)90047-X · DOI
  48. Plevris, 2014, Modeling of masonry failure surface under biaxial compressive stress using Neural Networks, Constr. Build. Mater., 55, 447, 10.1016/j.conbuildmat.2014.01.041 · DOI
  49. Bowden, 2005, Input determination for neural network models in water resources applications. Part 1—background and methodology, J. Hydrol., 301, 75, 10.1016/j.jhydrol.2004.06.021 · DOI
  50. Li, J., and Heap, A.D. (2008). A Review of Spatial Interpolation Methods for Environmental Scientists, Geoscience Australia.

Metadata

Authors
Faridmehr, I., Nehdi, M. L., Nikoo, M., Huseien, G. F., & Ozbakkaloglu, T.
Affiliations
  • South Ural State University (RU)
  • Western University (CA)
  • Islamic Azad University, Ahvaz Branch (IR)
  • National University of Singapore (SG)
  • Texas State University (US)
Corresponding author
Nehdi, Moncef L.
ORCID
Venue
Materials
Publisher
MDPI AG
Published
2021-05-05
Volume
14
Issue
9
Pages
2401
ISSN
1996-1944 (ISSN-L)
DOI
10.3390/ma14092401
PMC
PMC8125601
OpenAlex
W3157695626
Cited by
73
References
1
Citations by year
2026: 16 · 2025: 17 · 2024: 16 · 2023: 7 · 2022: 9 · 2021: 8
FWCI
5.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Materials (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Concrete and Cement Materials Research
  • Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
  • Physical Sciences › Engineering › Building and Construction › Materials Engineering and Processing
Keywords
alkali-activated materials, industrial by-products, alkali-activated mortar, embodied energy, CO2 emissions, cradle-to-gate life cycle assessment, artificial neural networks, cuckoo optimization algorithm, mix proportion optimization, mechanical properties, durability properties, carbon footprint reduction

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