Artificial neural networks test for the prediction of chemical stability of pyroclastic deposits-based AAMs and comparison with conventional mathematical approach (MLR)
Source abstract
Abstract
Abstract The investigation on the reticulation degree of volcanic alkali-activated materials, AAMs, were experimentally determined in terms of chemico-physical properties: weight loss after leaching test in water, ionic conductivity and pH of the leachate and compressive strength. Artificial neural network (ANN) was successfully applied to predict the chemical stability of volcanic alkali-activated materials. Nine input data per each chemico-physical parameter were used to train each ANN. The training series of specific volcanic precursors were tested also for the other one. Excellent correlations between experimental and calculated data of the same precursor type were found reaching values around one. The evidence of strong effect on chemical stability of the alkaline activator SiO2/Na2O molar ratio as well as the Si/Al ratio of precursor mixtures on the reticulation degree of ghiara-based formulation with respect to volcanic ash-based materials is presented. It must be noted that such effect was much less pronounced on the compressive strength values, appearing more insensitive the molar ratio of the alkaline activator. The comparison of the ANN results with more conventional multiple linear regression (MLR) testifies the higher prediction performance of the first method. MLRs results, less significant, are useful to confirm the powerful capacity of ANNs to identify the more suitable formulation using a set of experimental AAMs. This study, as few others, on the correlation between chemical stability and compressive strength of AAMs provide a great contribution in the direction of durability and in-life mechanical performance of these class of materials. Graphic abstract
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Recycling and utilization of industrial and municipal waste in materials production · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (50)
Show source-provided references
1. Davidovits J (1982) U.S. Patent No. 4,349,386
2. Habert G, D’Espinose De Lacaillerie JB, Roussel N (2011) An environmental evaluation of geopolymer based concrete production: Reviewing current research trends. J Clean Prod 19:1229–1238. https://doi.org/10.1016/j.jclepro.2011.03.01210.1016/j.jclepro.2011.03.012 ↗
3. Komnitsas KA (2011) Potential of geopolymer technology towards green buildings and sustainable cities. Proc Eng 21:1023–1032. https://doi.org/10.1016/j.proeng.2011.11.210810.1016/j.proeng.2011.11.2108 ↗
4. Liu Y, Shi C, Zhang Z, Li N (2019) An overview on the reuse of waste glasses in alkali-activated materials. Resour Conserv Recycl 144:297–309. https://doi.org/10.1016/j.resconrec.2019.02.00710.1016/j.resconrec.2019.02.007 ↗
5. Obonyo EA, Kamseu E, Lemougna PN et al (2014) A sustainable approach for the geopolymerization of natural iron-rich aluminosilicate materials. Sustain 6:5535–5553. https://doi.org/10.3390/su609553510.3390/su6095535 ↗
6. Irfan Khan M, Azizli K, Sufian S, et al (2014) Geopolymers as a sustainable binder of 21st century: a review. d010. https://doi.org/10.3390/wsf-4-d01010.3390/wsf-4-d010 ↗
7. Lancellotti I, Ponzoni C, Barbieri L, Leonelli C (2013) Alkali activation processes for incinerator residues management. Waste Manag 33:1740–1749. https://doi.org/10.1016/j.wasman.2013.04.01310.1016/j.wasman.2013.04.013 ↗
8. Komnitsas K, Zaharaki D (2007) Geopolymerisation: A review and prospects for the minerals industry. Miner Eng 20:1261–1277. https://doi.org/10.1016/j.mineng.2007.07.01110.1016/j.mineng.2007.07.011 ↗
9. Contrafatto L (2017) Recycled Etna volcanic ash for cement, mortar and concrete manufacturing. Constr Build Mater 151:704–713. https://doi.org/10.1016/j.conbuildmat.2017.06.12510.1016/j.conbuildmat.2017.06.125 ↗
10. Barone G, Mazzoleni P, Corsaro RA et al (2016) Nanoscale surface modification of Mt. Etna volcanic ashes. Geochim Cosmochim Acta 174:70–84. https://doi.org/10.1016/j.gca.2015.11.01110.1016/j.gca.2015.11.011 ↗
11. Tchakoute HK, Elimbi A, Yanne E, Djangang CN (2013) Utilization of volcanic ashes for the production of geopolymers cured at ambient temperature. Cem Concr Compos 38:75–81. https://doi.org/10.1016/j.cemconcomp.2013.03.01010.1016/j.cemconcomp.2013.03.010 ↗
12. Djobo JNY, Elimbi A, Tchakouté HK, Kumar S (2017) Volcanic ash-based geopolymer cements/concretes: the current state of the art and perspectives. Environ Sci Pollut Res 24:4433–4446. https://doi.org/10.1007/s11356-016-8230-810.1007/s11356-016-8230-8 ↗
13. Lemougna PN, Wang K, tuo, Tang Q, et al (2018) Review on the use of volcanic ashes for engineering applications. Resour Conserv Recycl 137:177–190. https://doi.org/10.1016/j.resconrec.2018.05.03110.1016/j.resconrec.2018.05.031 ↗
14. Kamseu E, Leonelli C, Perera DS et al (2009) Investigation of volcanic ash based geopolymers as potential building materials. Int Ceram Rev 58:136–140
15. Battiato G (1988) Le malte del centro storico di Catania. Doc dell’Istituto Dipartimentale di Archit e Urban dell’Università di Catania 16:85–107
16. Sciuto Patti C (1896) Sui materiali da costruzioni più usati in Catania. Tipografia Editrice dell’Etna, Catania
17. Belfiore CM, La Russa MF, Mazzoleni P et al (2010) Technological study of “ghiara” mortars from the historical city centre of Catania (Eastern Sicily, Italy) and petro-chemical characterisation of raw materials. Environ Earth Sci 61:995–1003. https://doi.org/10.1007/s12665-009-0418-510.1007/s12665-009-0418-5 ↗
18. Bultrini G, Fragala I, Ingo GM, Lanza G (2006) Minero-petrographic, thermal and microchemical investigation of historical mortars used in Catania (Sicily) during the XVII century A.D. Appl Phys A Mater Sci Process 83:529–536. https://doi.org/10.1007/s00339-006-3551-y10.1007/s00339-006-3551-y ↗
19. de Ferri L, Santagati C, Catinoto M et al (2019) A multi-technique characterization study of building materials from the Exedra of S. Nicolò l’Arena in Catania (Italy). J Build Eng 23:377–387. https://doi.org/10.1016/j.jobe.2019.01.02810.1016/j.jobe.2019.01.028 ↗
20. Mazzoleni P (2007) The use of volcanic stone in architecture: example of Etnean region. Acta Vulcanol 18:141–144
21. Barone G, Finocchiaro C, Lancellotti I et al (2020) Potentiality of the Use of Pyroclastic Volcanic Residues in the Production of Alkali Activated Material. Waste Biomass Valoriz. https://doi.org/10.1007/s12649-020-01004-610.1007/s12649-020-01004-6 ↗
22. Xu H, Van Deventer JSJ (2000) The geopolymerisation of alumino-silicate minerals. Int J Miner Process 59:247–266. https://doi.org/10.1016/S0301-7516(99)00074-510.1016/S0301-7516(99)00074-5 ↗
23. Duxson P, Fernández-Jiménez A, Provis JL et al (2007) Geopolymer technology: the current state of the art. J Mater Sci 42:2917–2933. https://doi.org/10.1007/s10853-006-0637-z10.1007/s10853-006-0637-z ↗
24. Lin YC, Fang X, Wang YP (2008) Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network. J Mater Sci 43:5508–5515. https://doi.org/10.1007/s10853-008-2832-610.1007/s10853-008-2832-6 ↗
25. Ning L (2009) Artificial neural network prediction of glass transition temperature of fluorine-containing polybenzoxazoles. J Mater Sci 44:3156–3164. https://doi.org/10.1007/s10853-009-3420-010.1007/s10853-009-3420-0 ↗
26. Garcia-Mateo C, Capdevila C, Caballero FG, De Andrés CG (2007) Artificial neural network modeling for the prediction of critical transformation temperatures in steels. J Mater Sci 42:5391–5397. https://doi.org/10.1007/s10853-006-0881-210.1007/s10853-006-0881-2 ↗
27. Liujie X, Jiandong X, Shizhong W et al (2007) Artificial neural network prediction of heat-treatment hardness and abrasive wear resistance of High-Vanadium High-Speed Steel (HVHSS). J Mater Sci 42:2565–2573. https://doi.org/10.1007/s10853-006-1278-y10.1007/s10853-006-1278-y ↗
28. Karakoç A, Keleş Ö (2020) A predictive failure framework for brittle porous materials via machine learning and geometric matching methods. J Mater Sci 55:4734–4747. https://doi.org/10.1007/s10853-019-04339-110.1007/s10853-019-04339-1 ↗
29. Bondar D (2014) Use of a neural network to predict strength and optimum compositions of natural alumina-silica-based geopolymers. J Mater Civ Eng 26:499–503. https://doi.org/10.1061/(ASCE)MT.1943-5533.000082910.1061/(ASCE)MT.1943-5533.0000829 ↗
30. Ling Y, Wang K, Wang X, Li W (2019) Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04662-310.1007/s00521-019-04662-3 ↗
31. Nazari A (2013) Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput Appl 23:507–518. https://doi.org/10.1007/s00521-012-0945-y10.1007/s00521-012-0945-y ↗
32. Sadat MR, Muralidharan K, Zhang L (2018) Reactive molecular dynamics simulation of the mechanical behavior of sodium aluminosilicate geopolymer and calcium silicate hydrate composites. Comput Mater Sci 150:500–509. https://doi.org/10.1016/j.commatsci.2018.04.04110.1016/j.commatsci.2018.04.041 ↗
33. Sadat MR, Bringuier S, Muralidharan K et al (2018) Atomic-scale dynamics and mechanical response of geopolymer binder under nanoindentation. Comput Mater Sci 142:227–236. https://doi.org/10.1016/j.commatsci.2017.10.02610.1016/j.commatsci.2017.10.026 ↗
34. Lau CK, Lee H, Vimonsatit V et al (2019) Abrasion resistance behaviour of fly ash based geopolymer using nanoindentation and artificial neural network. Constr Build Mater 212:635–644. https://doi.org/10.1016/j.conbuildmat.2019.04.02110.1016/j.conbuildmat.2019.04.021 ↗
35. Medri V, Fabbri S, Dedecek J et al (2010) Role of the morphology and the dehydroxylation of metakaolins on geopolymerization. Appl Clay Sci 50:538–545. https://doi.org/10.1016/j.clay.2010.10.01010.1016/j.clay.2010.10.010 ↗
36. Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River
37. Daniel G (2013) Principles of artificial neural networks - advanced series in circuits and systems (Vol. 7). World Scientific Publishing
38. Pala M, Özbay E, Öztaş A, Yuce MI (2007) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Constr Build Mater 21:384–394. https://doi.org/10.1016/j.conbuildmat.2005.08.00910.1016/j.conbuildmat.2005.08.009 ↗
39. Basheer IA, Hajmeer M (2000) Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods 43:3–31. https://doi.org/10.1016/S0167-7012(00)00201-310.1016/S0167-7012(00)00201-3 ↗
40. Shi H, Gao Y, Wang X (2010) Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48:955–962. https://doi.org/10.1007/s00170-009-2346-710.1007/s00170-009-2346-7 ↗
41. Liu SW, Huang JH, Sung JC, Lee CC (2002) Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 191:2831–2845. https://doi.org/10.1016/S0045-7825(02)00221-910.1016/S0045-7825(02)00221-9 ↗
42. Günaydin HM, Doǧan SZ (2004) A neural network approach for early cost estimation of structural systems of buildings. Int J Proj Manag 22:595–602. https://doi.org/10.1016/j.ijproman.2004.04.00210.1016/j.ijproman.2004.04.002 ↗
43. Özcan F, Atiş CD, Karahan O et al (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40:856–863. https://doi.org/10.1016/j.advengsoft.2009.01.00510.1016/j.advengsoft.2009.01.005 ↗
44. Yaprak H, Karaci A, Demir I (2013) Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Comput Appl 22:133–141. https://doi.org/10.1007/s00521-011-0671-x10.1007/s00521-011-0671-x ↗
45. Dou J, Yamagishi H, Pourghasemi HR et al (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776. https://doi.org/10.1007/s11069-015-1799-210.1007/s11069-015-1799-2 ↗
46. Saha A (2011) Data Mining in Excel. https://www.sites.google.com/site/sayhello2angshu/dminexcel
47. Argyrous G (2011) Statistics for research: with a guide to SPSS. Sage Publications, London
48. Montgomery DC (2013) Design and analysis of experiments. Wiley, London
49. Perai AH, Moghaddam HN, Asadpour S et al (2010) A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal. Poult Sci 89:1562–1568. https://doi.org/10.3382/ps.2010-0063910.3382/ps.2010-00639 ↗
50. Şahin M, Kaya Y, Uyar M (2013) Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data. Adv Sp Res 51:891–904. https://doi.org/10.1016/j.asr.2012.10.01010.1016/j.asr.2012.10.010 ↗