Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer se...
- Autores:
-
Awoyera, Paul
Kirgiz, Mehmet S.
amelec, viloria
Ovallos, David
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8041
- Acceso en línea:
- https://hdl.handle.net/11323/8041
https://doi.org/10.1016/j.jmrt.2020.06.008
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial neural networks
Genetic programming
Predictor
Response
Self-Compacting concrete
Geopolymers
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| title |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| spellingShingle |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques Artificial neural networks Genetic programming Predictor Response Self-Compacting concrete Geopolymers |
| title_short |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| title_full |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| title_fullStr |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| title_full_unstemmed |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| title_sort |
Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques |
| dc.creator.fl_str_mv |
Awoyera, Paul Kirgiz, Mehmet S. amelec, viloria Ovallos, David |
| dc.contributor.author.spa.fl_str_mv |
Awoyera, Paul Kirgiz, Mehmet S. amelec, viloria Ovallos, David |
| dc.subject.spa.fl_str_mv |
Artificial neural networks Genetic programming Predictor Response Self-Compacting concrete Geopolymers |
| topic |
Artificial neural networks Genetic programming Predictor Response Self-Compacting concrete Geopolymers |
| description |
There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-06-24 |
| dc.date.accessioned.none.fl_str_mv |
2021-03-18T13:08:21Z |
| dc.date.available.none.fl_str_mv |
2021-03-18T13:08:21Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
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22387854 |
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https://hdl.handle.net/11323/8041 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1016/j.jmrt.2020.06.008 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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22387854 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/8041 https://doi.org/10.1016/j.jmrt.2020.06.008 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
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[1] Hardjito D, Wallah S, Sumajouw D, Rangan B. Factors influencing the compressive strength of fly ash-based geopolymer concrete. Civ Eng Dimens 2004;6:88–93. [2] Rangan B. Fly ash-based geopolymer concrete. Curtin Univ Technol Perth 2008. [3] Oliveira MLS, Izquierdo M, Querol X, Lieberman RN, Saikia BK, Silva LFO. Nanoparticles from construction wastes: a problem to health and the environment. J Clean Prod 2019;219:236–43, http://dx.doi.org/10.1016/j.jclepro.2019.02.096. [4] Oliveira MLS, Tutikian BF, Milanes C, Silva LFO. Atmospheric contaminations and bad conservation effects in Roman mosaics and mortars of Italica. J Clean Prod 2020;248:119250, http://dx.doi.org/10.1016/j.jclepro.2019.119250 [5] MM A, Tutikian BF, Ortolan V, Oliveira MLS, Sampaio CH, Gómez PL, et al. Fire resistance performance of concrete-PVC panels with polyvinyl chloride (PVC) stay in place (SIP) formwork. J Mater Res Technol 2019;8:4094–107, http://dx.doi.org/10.1016/j.jmrt.2019.07.018. [6] Gómez-Plata L, Tutikian BF, Pacheco F, Oliveira MS, Murillo M, Silva LFO, et al. Multianalytical approach of stay-in-place polyvinyl chloride formwork concrete exposed to high temperatures. J Mater Res Technol 2020, http://dx.doi.org/10.1016/j.jmrt.2020.03.022 [7] Sathanandam T, Awoyera PO, Vijayan V, Sathishkumar K. Low carbon building: experimental insight on the use of fly ash and glass fibre for making geopolymer concrete. Sustain Environ Res 2017;27:146–53, http://dx.doi.org/10.1016/j.serj.2017.03.005. [8] Gallego-Cartagena E, Morillas H, Maguregui M, Patino-Camelo ˜ K, Marcaida I, Morgado-Gamero W, et al. A comprehensive study of biofilms growing on the built heritage of a Caribbean industrial city in correlation with construction materials. Int Biodeterior Biodegradation 2020;147:104874, http://dx.doi.org/10.1016/j.ibiod.2019.104874 [9] Silva LFO, Pinto D, Neckel A, Dotto GL, Oliveira MLS. The impact of air pollution on the rate of degradation of the fortress of Florianópolis Island. Brazil. Chemosphere 2020;251:126838, http://dx.doi.org/10.1016/j.chemosphere.2020.126838 [10] Silva LFO, Pinto D, Neckel A, Oliveira MLS, Sampaio CH. Atmospheric nanocompounds on Lanzarote Island: vehicular exhaust and igneous geologic formation interactions. Chemosphere 2020;254:126822, http://dx.doi.org/10.1016/j.chemosphere.2020.126822 [11] Morillas H, García-Florentino C, Marcaida I, Maguregui M, Arana G, Silva LFO, et al. In-situ analytical study of bricks exposed to marine environment using hand-held X-ray fluorescence spectrometry and related laboratory techniques. Spectrochim Acta Part B At Spectrosc 2018;1(46):28–35, http://dx.doi.org/10.1016/j.sab.2018.04.020. [12] Morillas H, Vazquez P, Maguregui M, Marcaida I, Silva LFO. Composition and porosity study of original and restoration materials included in a coastal historical construction. Constr Build Mater 2018;178:384–92, http://dx.doi.org/10.1016/j.conbuildmat.2018.05.168. [13] Morillas H, Maguregui M, Gallego-Cartagena E, Huallparimachi G, Marcaida I, Salcedo I, et al. Evaluation of the role of biocolonizations in the conservation state of Machu Picchu (Peru): the Sacred Rock. Sci Total Environ 2019;654:1379–88, http://dx.doi.org/10.1016/j.scitotenv.2018.11.299. [14] Castel A, Foster SJ. Bond strength between blended slag and Class F fly ash geopolymer concrete with steel reinforcement. Cem Concr Res 2015;72:48–53, http://dx.doi.org/10.1016/j.cemconres.2015.02.016. [15] Reed M, Lokuge W, Karunasena W. Fibre-reinforced geopolymer concrete with ambient curing for in situ applications. J Mater Sci 2014;49:4297–304, http://dx.doi.org/10.1007/s10853-014-8125-3 [16] Singh B, Ishwarya G, Gupta M, Bhattacharyya SK. Geopolymer concrete: a review of some recent developments. Constr Build Mater 2015;85:78–90, http://dx.doi.org/10.1016/j.conbuildmat.2015.03.036. [17] Part WK, Ramli M, Cheah CB. An overview on the influence of various factors on the properties of geopolymer concrete derived from industrial by-products. Constr Build Mater 2015;77:370–95, http://dx.doi.org/10.1016/j.conbuildmat.2014.12.065. [18] Heah CY, Kamarudin H, Mustafa Al Bakri AM, Binhussain M, Luqman M, Khairul Nizar I, et al. Effect of curing profile on kaolin-based geopolymers. Phys Procedia 2011;22:305–11, http://dx.doi.org/10.1016/j.phpro.2011.11.048. [19] Nagalia G, Park Y, Ph D, Asce M, Abolmaali A, Ph D, et al. Compressive strength and microstructural properties of fly ash – based geopolymer concrete. J Mater Civ Eng 2016:1–11, http://dx.doi.org/10.1061/(ASCE)MT.1943-5533.0001656. [20] Nematollahi B, Sanjayan J, Chai JXH, Lu TM. Properties of fresh and hardened glass Fiber reinforced fly ash based geopolymer concrete. Key Eng Mater 2014;594–595:629–33, http://dx.doi.org/10.4028/www.scientific.net/KEM.594-595.629. [21] Santana HA, Andrade Neto JS, Amorim NS Junior, Ribeiro DV, Cilla MS, Dias CMR. Self-compacting geopolymer mixture: dosing based on statistical mixture design and simultaneous optimization. Constr Build Mater 2020;249:118677, http://dx.doi.org/10.1016/j.conbuildmat.2020.118677. [22] Demie S, Nuruddin MF, Shafiq N. Effects of micro-structure characteristics of interfacial transition zone on the compressive strength of self-compacting geopolymer concrete. Constr Build Mater 2013;41:91–8, http://dx.doi.org/10.1016/j.conbuildmat.2012.11.067. [23] Memon FA, Nuruddin MF, Shafiq N. Effect of silica fume on the fresh and hardened properties of fly ash-based self-compacting geopolymer concrete. Int J Miner Metall Mater 2013;20:205–13, http://dx.doi.org/10.1007/s12613-013-0714-7 [24] Nuruddin MF, Demie S, Shafiq N. Effect of mix composition on workability and compressive strength of self-compacting geopolymer concrete. Am J Civ Eng Archit 2011;38:1196–203, http://dx.doi.org/10.1139/l11-077. [25] Karthika V, Awoyera PO, Akinwumi II, Gobinath R, Gunasekaran R, Lokesh N. Structural properties of lightweight self-compacting concrete made with pumice stone and mineral admixtures. Rev Rom Mater Rom J Mater 2018;48 [26] Palanisamy M, Poongodi K, Awoyera PO, Ravindran G. Permeability properties of lightweight self-consolidating concrete made with coconut shell aggregate. Integr Med Res 2020, http://dx.doi.org/10.1016/j.jmrt.2020.01.092. [27] Adesina A, Awoyera P. Overview of trends in the application of waste materials in self-compacting concrete production. SN Appl Sci 2019, http://dx.doi.org/10.1007/s42452-019-1012-4. [28] Awoyera P. Nonlinear finite element analysis of steel fibre-reinforced concrete beam under static loading. J Eng Sci Technol 2016;11:1–9. [29] Sadrmomtazia A, Sobhanib J, Mirgozar M. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Constr Build Mater 2013;42:205–16. [30] Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. Int J Forecast 1998;14:35–62 [31] Mansouri I, Azmathulla HM, Hu JW. Gene expression programming application for prediction of ultimate axial strain of FRP-confined concrete. Electron J Fac Civ Eng Osijek-e-GFOS 2018;16:64–76. [33] Topc B. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Material Science 2008;41:305–11, http://dx.doi.org/10.1016/j.commatsci.2007.04.009. [34] Bhatti MA. Predicting the compressive strength and slump of high strength concrete using neural network. Construction and Building Material 2006;20:769–75, http://dx.doi.org/10.1016/j.conbuildmat.2005.01.054. [35] Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv Eng Softw 2009;40:920–7, http://dx.doi.org/10.1016/j.advengsoft.2008.12.008. [36] Hong-guang N, Ji-zong W. Prediction of compressive strength of concrete by neural networks. xxx 2000;30:1245–50. [37] Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Constr Build Mater 2010;24:709–18, http://dx.doi.org/10.1016/j.conbuildmat.2009.10.037. [38] Garzón-roca J, Marco CO, Adam JM. Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Eng Struct 2013;48:21–7, http://dx.doi.org/10.1016/j.engstruct.2012.09.029. [39] Chen L. Grey and neural network prediction of concrete compressive strength using physical properties of electric arc furnace oxidizing slag. J Environ Eng Manag 2010;20:189–94 [40] Awoyera PO, Akinmusuru JO, Krishna AS, Gobinath R, Arunkumar B, Sangeetha G. Model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles. Soft Comput Probl Solving Adv Intell Syst Comput 2018;1:197–207, http://dx.doi.org/10.1007/978-981-15-0035-0 15. [41] Arun Kumar B, Sangeetha G, Srinivas A, Awoyera P, Gobinath R, Venkata Raman V. Models for predictions of mechanical properties of low-density self-compacting concrete prepared from mineral admixtures and pumice stone. Adv Intell Syst Computnd 2019. 42] Shafabakhsh G, Jafari Ani O, Talebsafa M. Artificial neural network modeling (ANN) for predicting rutting performance of nano- modified hot-mix asphalt mixtures containing steel slag aggregates. Constr Build Mater J 2015;85:136–43, http://dx.doi.org/10.1016/j.conbuildmat.2015.03.060. [43] Hodhod O, Ahmed HI, Hodhod OA, Ahmed HI. Modeling the corrosion initiation time of slag concrete using the artificial neural network modeling the corrosion initiation time of slag concrete using the artificial neural network. Hbrc J 2014:8–12, http://dx.doi.org/10.1016/j.hbrcj.2013.12.002 [44] Carmichael RP. Relationships between young’s modulus, compressive strength, poisson’s ratio, and time for early age concrete 2009; 2020. [45] Bal L, Buyle-bodin F. Artificial neural network for predicting drying shrinkage of concrete. Constr Build Mater 2013;38:248–54, http://dx.doi.org/10.1016/j.conbuildmat.2012.08.043. [46] Duan Z, Kou S, Poon C. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr Build Mater 2013;40:1200–6. 47] Bilim C, Atis CD, Tanyildizi H, Karahan O. Advances in engineering software predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network, 40; 2009. p. 334–40, http://dx.doi.org/10.1016/j.advengsoft.2008.05.005 [48] Barbuta M, Diaconescu R, Harja M. Using neural networks for prediction of properties of polymer concrete with fly ash. J Mater Civ Eng 2012;24:523–8, http://dx.doi.org/10.1061/(ASCE)MT.1943-5533.0000413. [49] Nazari A, Torgal FP. Predicting compressive strength of different geopolymers by artificial neural networks. Ceram Int 2013;39:2247–57, http://dx.doi.org/10.1016/j.ceramint.2012.08.070. [50] Yadollahi MM, Benli A, Demirboga˘ R. Prediction of compressive strength of geopolymer composites using an artificial neural network Prediction of compressive strength of geopolymer composites using an artificial neural network. Mater Res Innov 2016;19:453–8, http://dx.doi.org/10.1179/1433075X15Y.0000000020. [51] Nazari A. Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput Appl 2013;23:507–18, http://dx.doi.org/10.1007/s00521-012-0945-y [52] Ushaa T, Anuradha R, Venkatasubramani G. Performance of self-compacting geopolymer concrete containing different mineral edmixtures. Indian J Eng Mater Sci 2015;22:473–81. [53] BS 882. Aggregates from natural sources; 1992. Br Stand London, UK. [54] BS 812-110. Methods for determination of aggregate crushing value (ACV); 1990. Br Stand London, UK. [55] BS EN 1097-6. Tests for mechanical and physical properties of aggregates; 1995. Br Stand London, UK. [56] Sashidhar C, Guru Jawahar J, Neelima C, Pavan Kumar D. Preliminary studies on self compacting geopolymer concrete using manufactured sand. Asian J Civ Eng 2016;17:277–88. [57] Nuruddin M, Demie S, Shafiq N. Effect of mix composition on workabilit. . .of self-compacting geopolymer concrete.pDf. Can. J Civ Eng 2011;38:1196–203. [58] Fareed A, Muhd F, Sadaqatullah K, Nasir S, Tehmina A. Effect of sodium hydroxide concentration on fresh properties and compressive strength of self-compacting geopolymer concrete. J Eng Sci Technol 2013;8:44–56. [59] EFNARC. Specification and guildelines for self-compacting conrete; 2002 [61] Alshihri MM, Azmy AM, El-bisy MS. Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 2009;23:2214–9, http://dx.doi.org/10.1016/j.conbuildmat.2008.12.003 [62] Lee S. Prediction of concrete strength using artificial neural networks. xxx 2003;25:849–57, http://dx.doi.org/10.1016/S0141-0296(03)00004-X. [63] Pala M, Özbay E, Öztas A, Yüce M. Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Constr Build Mater 2007;21:384–94. [64] Sangeetha G., Arun Kumar B., Srinivas A., Gobinath R., Awoyera P. Optimization of drilling rig hydraulics in drilling operations using soft computing techniques. Adv Intell Syst Comput n.d [65] Zurada J. Introduction to artificial neural systems. Info Access Distrib Ltd; 1992. [66] Koza JR. Genetic programming: on the programming of computers by means of natural selection; 1992. [67] Liu SW, Huang JH, Sung JC, Lee CC. Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 2002;191:2831–45, http://dx.doi.org/10.1016/S0045-7825(02)00221-9 [68] Nazari A. RETRACTED ARTICLE: application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer. Neural Comput Appl 2019;31:767–76, http://dx.doi.org/10.1007/s00521-012-1137-5 [69] Tanyildizi H, Özcan F, Atis CD, Karahan O, Uncuog E. Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 2009;40:856–63, http://dx.doi.org/10.1016/j.advengsoft.2009.01.005. [70] Farzampour A, Mansouri I, Mortazavi SJ, Hu JW. Force-displacement relationship of a butterfly-shaped beams based on gene expression programming. Korea: 10th Int. Symp. Steel Struct., Jeju; 2019. p. 10–3. |
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Awoyera, PaulKirgiz, Mehmet S.amelec, viloriaOvallos, David2021-03-18T13:08:21Z2021-03-18T13:08:21Z2020-06-2422387854https://hdl.handle.net/11323/8041https://doi.org/10.1016/j.jmrt.2020.06.008Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/There has been a persistent drive for sustainable development in the concrete industry. While there are series of encouraging experimental research outputs, yet the research field requires a standard framework for the material development. In this study, the strength characteristics of geopolymer self-compacting concrete made by addition of mineral admixtures, have been modelled with both genetic programming (GEP) and the artificial neural networks (ANN) techniques. The study adopts a 12M sodium hydroxide and sodium silicate alkaline solution of ratio to fly ash at 0.33 for geopolymer reaction. In addition to the conventional material (river sand), fly ash was partially replaced with silica fume and granulated blast furnace slag. Various properties of the concrete, filler ability and passing ability of fresh mixtures, and compressive, split-tensile and flexural strength of hardened concrete were determined. The model developmentinvolved using raw materials and fresh mix properties as predictors, and strength properties as response. Results shows that the use of the admixtures enhanced both the fresh and hardened properties of the concrete. Both GEP and ANN methods exhibited good prediction of the experimental data, with minimal errors. However, GEP models can be preferred as simple equations are developed from the process, while ANN is only a predictor.Awoyera, Paul-will be generated-orcid-0000-0002-6212-5090-600Kirgiz, Mehmet S.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Ovallos, David-will be generated-orcid-0000-0003-0836-2287-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Materials Research and Technologyhttps://www.sciencedirect.com/science/article/pii/S2238785420314095Artificial neural networksGenetic programmingPredictorResponseSelf-Compacting concreteGeopolymersEstimating strength properties of geopolymer self-compacting concrete using machine learning techniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Hardjito D, Wallah S, Sumajouw D, Rangan B. Factors influencing the compressive strength of fly ash-based geopolymer concrete. Civ Eng Dimens 2004;6:88–93.[2] Rangan B. Fly ash-based geopolymer concrete. Curtin Univ Technol Perth 2008.[3] Oliveira MLS, Izquierdo M, Querol X, Lieberman RN, Saikia BK, Silva LFO. Nanoparticles from construction wastes: a problem to health and the environment. J Clean Prod 2019;219:236–43, http://dx.doi.org/10.1016/j.jclepro.2019.02.096.[4] Oliveira MLS, Tutikian BF, Milanes C, Silva LFO. Atmospheric contaminations and bad conservation effects in Roman mosaics and mortars of Italica. J Clean Prod 2020;248:119250, http://dx.doi.org/10.1016/j.jclepro.2019.119250[5] MM A, Tutikian BF, Ortolan V, Oliveira MLS, Sampaio CH, Gómez PL, et al. Fire resistance performance of concrete-PVC panels with polyvinyl chloride (PVC) stay in place (SIP) formwork. 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Steel Struct., Jeju; 2019. p. 10–3.PublicationORIGINALEstimating strength properties of geopolymer self-compacting concrete using machine learning techniques.pdfEstimating strength properties of geopolymer self-compacting concrete using machine learning techniques.pdfapplication/pdf2774916https://repositorio.cuc.edu.co/bitstreams/f31243a0-eeff-4267-93af-ccb339549b03/download0f5313a15b40453e89041cf6df8a86f2MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/4eae1a21-b6cb-4e17-af9b-2c7af38ce761/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/76d51e1d-b6fd-4292-9da6-26cf9b3b7f79/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILEstimating strength properties of geopolymer self-compacting concrete using machine learning techniques.pdf.jpgEstimating strength properties of geopolymer self-compacting concrete using machine learning 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