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Artificial Neural Network Approach to Predict Carbonation Depth in Metakaolin, Brick Powder and Calcined Sediments-Modified Mortars

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13 gru 2024

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Liu K., Alam M.S., Zhu J., Zheng J. & Chi L. (2021). Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms. Constr. Build. Mater. 301, 124382. DOI: 10.1016/j.conbuildmat.2021.124382. Search in Google Scholar

Schneider M. (2019). The cement industry on the way to a low-carbon future. Cem. Concr. Res. 124, 105792. DOI: 10.1016/j.cemconres.2019.105792. Search in Google Scholar

Robbie-Andrew M. (2018). Global CO2 emissions from cement production. J. Earth. Syst. Sci. Data. 10, 195-217. DOI: 10.5194/essd-10-195-2018. Search in Google Scholar

Benhelal E., Shamsaei E. & Rashid M.I. (2021). Challenges against CO2 abatement strategies in cement industry: A review. J. Environ. Sci. 104, 84-101. DOI: 10.1016/j.jes.2020.11.020. Search in Google Scholar

Chatterjee A. & Sui T. (2019). Alternative fuels–effects on clinker process and properties. Cem. Concr. Res. 123, 105777. DOI: 10.1016/j.cemconres.2019.105777. Search in Google Scholar

Yang D.Y., Liu M. & Ma Z.M. (2020). Properties of the foam concrete containing waste brick powder derived from construction and demolition waste. J. Build. Eng. 32, 101509. DOI: 10.1016/j.jobe.2020.101509. Search in Google Scholar

Zhao Y., Gao J., Liu C., Chen X. & Xu Z. (2020). The particle-size effect of waste clay brick powder on its pozzolanic activity and properties of blended cement. J. Clean. Prod. 242, 118521. DOI: 10.1016/j.jclepro.2019.118521. Search in Google Scholar

Güneyisi E. & Mermerdaş K. (2007). Comparative study on strength, sorptivity, and chloride ingress characteristics of air-cured and water-cured concretes modified with metakaolin. Mater. Struct. 40, 1161-1171. DOI: 10.1617/s11527-007-9258-5. Search in Google Scholar

Komnitsas K., Zaharaki D., Vlachou A., Bartzas G. & Galetakis M. (2015). Effect of synthesis parameters on the quality of construction and demolition wastes (CDW) geopolymers. Adv. Powder. Technol. 26, 368–376. DOI: 10.1016/j.apt.2014.11.012. Search in Google Scholar

Parande A.K., Babu B.R., Karthik M.A., Kumaar K.D. & alaniswamy N. (2008). Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar. Constr. Build. Mater. 22, 127–134. DOI: 10.1016/j.conbuildmat.2006.10.003. Search in Google Scholar

Said-Mansour M., Kadri E.H., Kenai S., Ghrici M. & Bennaceur R. (2011). Influence of calcined kaolin on mortarproperties. Constr. Build. Mater. 25, 2275-2282. DOI: 10.1016/j.conbuildmat.2010.11.017. Search in Google Scholar

Ouldkhaoua Y., Benabed B., Abousnina R., Kadri E.H. & Khatib J. (2020). Effect of using metakaolin as supplementary cementitious material and recycled CRT funnel glass as fine aggregate on the durability of green self-compacting concrete. Constr. Build. Mater. 235, 117802. DOI: 10.1016/j.conbuildmat.2019.117802. Search in Google Scholar

Khatib J.M. & Clay R.M. (2004). Absorption characteristics of metakaolin concrete. Cem. Concr. Res. 34, 19-29. DOI: 10.1016/S0008-8846(03)00188-1. Search in Google Scholar

Vejmelková E., Keppert M., Grzeszczyk S., Skaliński B. & Černý R. (2011). Properties of self-compacting concrete mixtures containing metakaolin and blast furnace slag. Constr. Build. Mater. 25, 1325-1331. DOI: 10.1016/j.conbuildmat.2010.09.012. Search in Google Scholar

Bernal S.A., De Gutiérrez R.M. & Provis J.L. (2012). Engineering and durability properties of concretes based on alkali-activated granulated blast furnace slag/metakaolin blends. Constr. Build. Mater. 33, 99-108. DOI: 10.1016/j.conbuildmat.2012.01.017. Search in Google Scholar

Badogiannis E. & Tsivilis S. (2009). Exploitation of poor Greek kaolins: Durability of metakaolin concrete. Cem. Concr. Compos. 31, 128-133. DOI: 10.1016/j.cemconcomp.2008.11.001. Search in Google Scholar

Barbhuiya S., Chow P. & Memon S. (2015). Microstructure, hydration and nanomechanical properties of concrete containing metakaolin. Constr. Build. Mater. 95, 696-702. DOI: 10.1016/j.conbuildmat.2015.07.101. Search in Google Scholar

Schackow A., Stringari D., Senff L., Correia S.L. & Segadães A.M. (2015). Influence of fired clay brick waste additions on the durability of mortars. Cem. Concr. Compos. 62, 82–89. DOI: 10.1016/j.cemconcomp.2015.04.019. Search in Google Scholar

Silpa K., Lisa Y., Perinaz B.T. & Frank V.W. (2018). What a waste 2.0: a global snapshot of solid waste management to 2050, World Bank Publications. https://openknowledge.worldbank.org/handle/10986/30317. Search in Google Scholar

Pasupathy K., Ramakrishnan S. & Sanjayan J. (2021). Formulating eco-friendly geopolymer foam concrete by alkali-activation of ground brick waste. J. Clean. Prod. 325, 129180. DOI: 10.1016/j.jclepro.2021.129180. Search in Google Scholar

Ma Z.M., Liu M., Tang Q., Liang C.F. & Duan Z. (2019). Chloride permeability of recycled aggregate concrete under the coupling effect of freezing-thawing, elevated temperature or mechanical damage. Constr. Build. Mater. 237, 117648. DOI: 10.1016/j.conbuildmat.2019.117648. Search in Google Scholar

Grellier A., Bulteel D., Bouarroudj M.E.K., Rémond S., Zhao Z. & Courard L. (2021). Alternative hydraulic binder development based on brick fines: Influence of particle size and substitution rate. J. Build. Eng. 39, 102263. DOI: 10.1016/j.jobe.2021.102263. Search in Google Scholar

Safhi A.M., Rivard P., Yahia A., Benzerzour M. & Khayat K.H. (2021). Valorization of dredged sediments in self-consolidating concrete: fresh, hardened, and microstructural properties. J. Clean. Prod. 263, 121472. DOI: 10.1016/j.jclepro.2020.121472. Search in Google Scholar

Sadok R.H., Belas N., Tahlaiti M. & Mazouzi R. (2021). Reusing calcined sediments from Chorfa II dam as partial replacement of cement for sustainable mortar production. J. Build. Eng. 40, 102273. DOI: 10.1016/j.jobe.2021.102273. Search in Google Scholar

Jaglal K., Crawford D.M., Anagnost S.W. & White B.E. (2017). Alternative Approaches for Managing Dredged Sediments, in: Proceeding of the Western Dredging Association (WEDA), Vancouver, British Columbia, Canada. Search in Google Scholar

Ortega J.M., Letelier V., Solas C., Moriconi G., Climent M.A. & Sánchez I. (2018). Longterm effects of waste brick powder addition in the microstructure and service properties of mortars. Constr. Build. Mater. 182, 691– 702. DOI: 10.1016/j.conbuildmat.2018.06.161. Search in Google Scholar

Li L.G., Lin Z.H., Chen G.M. & Kwan A.K.H. (2020). Reutilizing clay brick dust as paste substitution to produce environment-friendly durable mortar. J. Clean. Prod. 274, 122787. DOI: 10.1016/j.jclepro.2020.122787. Search in Google Scholar

Kazi Aoual-Benslafa F., Kerdal D., Ameur M., Mekerta B. & Semcha A. (2015). Durability of mortars made with dredged sediments. Proc. Eng. 118, 240–25. DOI: 10.1016/j.proeng.2015.08.423. Search in Google Scholar

Safer O., Belas N., Belaribi O., Belguesmia K., Bouhamou N.E. & Mebrouki A. (2018). Valorization of dredged sediments as a component of vibrated concrete: durability of these concretes against sulfuric acid attack. Int. J. Concr. Struct. Mater. 12, 44. DOI: 10.1186/s40069-018-0270-7. Search in Google Scholar

Belguesmia K. (2018). Optimization of an Eco-BAP Based on Dredged Sediment of Fresh, Hardened and Durability, Thesis. Abdelhamid Ibn Badis University of Mostaganem. Search in Google Scholar

Taffese W.Z. &Sistonen E. (2013). Service life prediction of repaired structures using concrete recasting method: state-of-the-art. Proc. Eng. 57, 1138–114. DOI: 10.1016/j.proeng.2013.04.143. Search in Google Scholar

Kari O.P., Puttonen J. & Skantz E. (2014). Reactive transport modelling of long-term carbonation. Cem. Concr. Compos. 52, 42–53. DOI: 10.1016/j.cemconcomp.2014.05.003. Search in Google Scholar

Taffese W.Z., Sistonen E. & Puttonen J. (2015). CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods. Constr. Build. Mater. 100, 70-82. DOI: 10.1016/j.conbuildmat.2015.09.058. Search in Google Scholar

Yang K.H., Singh J., Lee B.Y. & Kwon S.J. (2017). Simple technique for tracking chloride penetration in concrete based on the crack shape and width under steady-state conditions. Sustainability. 9, 282. Search in Google Scholar

Papadakis V.G. (2000). Effect of supplementary cementing materials on concrete resistance against carbonation and chloride ingress. Cem. Concr. Res. 30, 291-299. DOI: 10.1016/S0008-8846(99)00249-5. Search in Google Scholar

Morandeau A., Thiery M. & Dangla P. (2014). Investigation of the carbonation mechanism of CH and CSH in terms of kinetics, microstructure changes and moisture properties. Cem. Concr. Res. 56, 153-170. DOI: 10.1016/j.cemconres.2013.11.015. Search in Google Scholar

Duan P., Shui Z., Chen W. & Shen C. (2015). Enhancing microstructure and durability of concrete from ground granulated blast furnace slag and metakaolin as cement replacement materials. J. Mater. Res. Techno. 2, 52-59. DOI: 10.1016/j.jmrt.2013.03.010. Search in Google Scholar

Bernal S.A. (2015). Effect of the activator dose on the compressive strength and accelerated carbonation resistance of alkali silicate-activated slag/metakaolin blended materials. Constr. Build. Mater. 98, 217-226. DOI: 10.1016/j.conbuildmat.2015.08.013. Search in Google Scholar

Singh N., Mithulraj M. & Arya S. (2019). Utilization of coal bottom ash in recycled concrete aggregates based self compacting concrete blended with metakaolin, Resources. Conser. Recycl. 144, 240-251. DOI: 10.1016/j.resconrec.2019.01.044. Search in Google Scholar

Güneyisi E., Gesoğlu M. & Mermerdaş K. (2008). Improving strength, drying shrinkage, and pore structure of concrete using metakaolin. Mater. Struct. 41, 937-949. DOI: 10.1617/s11527-007-9296-z. Search in Google Scholar

Ramezanianpour A.A. & Jovein H.B. (2012). Influence of metakaolin as supplementary cementing material on strength and durability of concretes. Constr. Build. Mater. 30, 470-479. DOI: 10.1016/j.conbuildmat.2011.12.050. Search in Google Scholar

Poon C.S., Kou S.C. & Lam L. (2006). Compressive strength, chloride diffusivity and pore structure of high performance metakaolin and silica fume concrete. Constr. Build. Mater. 20, 858-865. DOI: 10.1016/j.conbuildmat.2005.07.001. Search in Google Scholar

Safer O. (2017). Optimization of the formulation of an eco-concrete based on dredging sediments and study of its resistance to chemical attacks. PhD Dissertation. Mostaganem University, Algeria. Search in Google Scholar

Mikhailenko P., Cassagnabère F., Emam A. & Lachemi M. (2018). Influence of physico-chemical characteristics on the carbonation of cement paste at high replacement rates of metakaolin. Constr. Build. Mater. 158, 164-172. DOI: 10.1016/j.conbuildmat.2017.10.021. Search in Google Scholar

Shao J.H. (2019). Hydration performance and carbonation of clay brick powder-cement complex cementitious material. Doctoral dissertation, Master thesis, Southeast University. China. Search in Google Scholar

Ekolu S.O. (2018). Model for practical prediction of natural carbonation in reinforced concrete: Part 1-formulation. Cem. Concr. Compos. 86, 40–56. DOI: 10.1016/j.cemconcomp.2017.10.006. Search in Google Scholar

Félix E.F., Carrazedo R. & Possan E. (2017). Parametric analysis of carbonation process in reinforced concrete structures through Artificial Neural Networks. Revista. ALCONPAT. 7, 302–316. DOI: 10.21041/ra.v7i3.245. Search in Google Scholar

Van Balen K. & Van Gemert D. (1994). Modelling lime mortar carbonation. Mater. Struct. 27, 393-398. Search in Google Scholar

Papadakis V.G., Vayenas C.G. & Fardis M.N. (1991). Fundamental modelling and experimental investigation of concrete carbonation. Mater. J. 88, 363-373. Search in Google Scholar

Haykin S. (2007). Neural Networks: A Comprehensive Foundation, Prentice-Hall Inc. Search in Google Scholar

Moradi M.J., Khaleghi M., Salimi J., Farhangi V. & Ramezanianpour A.M. (2021). Predicting the compressive strength of concrete containing metakaolin with different properties using ANN. Meas.183, 109790. DOI: 10.1016/j.measurement.2021.109790. Search in Google Scholar

Felix E.F., Carrazedo R. & Possan E. (2021). Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis. Constr. Build. Mater. 266, 121050. DOI: 10.1016/j.conbuildmat.2020.121050. Search in Google Scholar

Akpinar P. & Uwanuakwa F.I.D. (2016). Intelligent prediction of concrete carbonation depth using neural networks, Bull. Transilvania Univ. Brasov. Math. Informat. Phys. Series III.9, 99. Search in Google Scholar

Lu C. & Liu R. (2009). Predicting carbonation depth of prestressed concrete under different stress states using artificial neural network. Adv. Artificial. Neural. Syst. DOI: 10.1155/2009/193139. Search in Google Scholar

Kwon S.J. & Song H.W. (2010). Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modelling. Cem. Concr. Res. 40, 119–127. DOI: 10.1016/j.cemconres.2009.08.022. Search in Google Scholar

Han-Seung L. & Wang X.Y. (2016). Evaluation of compressive strength development and carbonation depth of high volume slag-blended concrete. Constr. Build. Mater. 124, 45–54. DOI: 10.1016/j.conbuildmat.2016.07.070. Search in Google Scholar

Londhe S.N., Kulkarni P.S., Dixit P.R., Silva A., Neves R. & Brito J. (2021). Predicting carbonation coefficient using Artificial neural networks and genetic programming. J. Build. Eng. 39, 102258. DOI: 10.1016/j.jobe.2021.102258. Search in Google Scholar

Possan E., Thomaz W.A., Aleandri G.A., Felix E.F. & dos Santos A.C. (2017). CO2 uptake potential due to concrete carbonation: a case study. Case. Stud. Constr. Mater. 6, 147–161. DOI: 10.1016/j.cscm.2017.01.007. Search in Google Scholar

Luo D., Niu D. & Dong Z. (2014). Application of neural network for concrete carbonation depth prediction. 4th International Conference on the Durability of Concrete Structures. Search in Google Scholar

Carevic V., Ignjatovic I. & Dragaš J. (2019). Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete. Constr. Build. Mater. 213, 194–208. DOI: 10.1016/j.conbuildmat. 2019.03.267. Search in Google Scholar

Kellouche Y., Boukhatem B., Ghrici M. & Tagnit-Hamou A. (2019). Exploring the major factors affecting flyash concrete carbonation using artificial neural network. Neural. Comput. Appl. 31, 969–988. DOI: 10.1007/s00521- 017-3052-2. Search in Google Scholar

Jiang L., Lin B. & Cai Y. (2000). A model for predicting carbonation of high-volume fly ash concrete. Cem. Concr. Res. 30, 699-702. DOI: 10.1016/S0008-8846(00)00227-1. Search in Google Scholar

Chérifi W.N.E.H., Houmadi Y. & Aissa Mamoune S.M. (2022). Prediction of corrosion potential using the generalized artificial neural networks method. Can. J. Civ. Eng. 49(6), 1040-1048. DOI: 10.1139/cjce-2020-0712. Search in Google Scholar

Limeira J., Etxeberria M., Agullo L. & Molina D. (2011). Mechanical and durability properties of concrete made with dredged marine sand, Constr. Build. Mater. 25(11), 4165–4174. DOI: 10.1016/j.conbuildmat.2019.117178. Search in Google Scholar

EN 197-1 (2011). Standard Cement – Part 1- Composition, specifications and conformity criteria for common cements. Search in Google Scholar

Afnor, NF EN 196-1 (2016). Methods of Testing Cement - Part 1: Determination of Strength. Search in Google Scholar

AFGC–AFREM (1997). Recommended Method for Durability Indicator, Proceedings of the Technical Meeting of AFPC–AFREM, Toulouse, France. Search in Google Scholar

Auroy M., Poyet S., Le Bescop P., Torrenti J.M., Charpentier T., Moskura M. & Bourbon X. (2018). Comparison between natural and accelerated carbonation (3% CO2): Impact on mineralogy, microstructure, water retention and cracking, Cem. Concr. Res. 109, 64-80. DOI: 10.1016/j.cemconres.2018.04.012. Search in Google Scholar

Benjamin J.R. & Cornell C.A. (1970). Probability, Statistics and Decision for Civil Engineers, Mcgraw-Hill Book Company, New York, USA. 684. ISBN 07004549-6. Search in Google Scholar

Subir P. (2012). Modelling to Study the Effect of Environmental Parameters on Corrosion of Mild Steel in Seawater Using Neural Network. Int. Sch. Res. Netw. ISRN Metallurgy: 6. DOI: 10.5402/2012/487351. Search in Google Scholar

Haykin S. (2009). Neural networks and learning machines, 3rdedn. Pearson Education Inc, Upper Saddle River. Search in Google Scholar

Abiodun O.I., Jantan A., Omolara A.E., Dada K.V., Mohamed N.A. & Arshad H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon. 4, e00938. DOI: 10.1016/j.heliyon.2018.e00938. Search in Google Scholar

Adhikary B.B. & Mutsuyoshi H. (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Constr. Build. Mater. 20, 801–811. DOI: 10.1016/j.conbuildmat.2005.01.047. Search in Google Scholar

Hosseinpour M., Sharifi H. & Sharifi Y. (2018). Stepwise regression modelling for compressive strength assessment of mortar containing metakaolin. Int. J. Model. Simul. 38, 207–215. DOI: 10.1080/02286203.2017.1422096. Search in Google Scholar

McCulloch W. & Pitts W. (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133. DOI: 10.1007/BF02478259. Search in Google Scholar

Rahali B., Aissa Mamoune S.M. & Trouzine H. (2018). Using artificial neural networks approach to estimate compressive strength for rubberized concrete. Period. Polytech. Civ. Eng. 62, 858-865. DOI: 10.3311/PPci.11928. Search in Google Scholar

Merouane F.Z. & Aissa Mamoune S.M. (2019). Prediction of Swelling Parameters of Two Clayey Soils from Algeria using Artificial Neural Networks. Mathe. Model. Civ. Eng. 14(3), 11-26. DOI: 10.2478/mmce-2018-0008. Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Inżynieria, Wstępy i przeglądy, Inżynieria, inne