Advances in nano research

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Advancing nanomaterials research: A comprehensive review of artificial intelligence applications in geotechnical properties

  • Ahmed Cemiloglu (School of Information Engineering, Yancheng Teachers University) ;
  • Licai Zhu (School of Information Engineering, Yancheng Teachers University) ;
  • Sibel Arslan (Faculty of Technology, Sivas Cumhuriyet University) ;
  • Yaser A. Nanehkaran (School of Information Engineering, Yancheng Teachers University) ;
  • Mohammad Azarafza (Geotechnical Unit, Faculty of Civil Engineering, University of Tabriz) ;
  • Reza Derakhshani (Department of Earth Sciences, Utrecht University)
  • Received : 2023.07.10
  • Accepted : 2024.10.31
  • Published : 2024.12.25
⟨ Previous Next ⟩

Abstract

This article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI's ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI's capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI's capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry.

Keywords

References

  1. Abbasi, N., Farjad, A. and Sepehri, S. (2017), "The use of nanoclay particles for stabilization of dispersive clayey soils", Geotech. Geol. Eng., 36(1), 327-335. https://doi.org/10.1007/s10706-017-0330-9.
  2. Abrougui, K., Gabsi, K., Mercatoris, B., Khemis, C., Amami, R. and Chehaibi, S. (2019), "Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR)", Soil Tillage Res., 190, 202-208. https://doi.org/10.1016/j.still.2019.01.011.
  3. Adeli, H. (2001), "Neural networks in civil engineering: 1989–2000", Comput. Aid. Civil Infrastruct. Eng., 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219.
  4. Akbulut, S. (2005), "Artificial neural networks for predicting the hydraulic conductivity of coarse-grained soils", Eurasian Soil Sci. Pochvovedenie, 38(4), 392-398.
  5. Alavi, A.H., Gandomi, A.H., Mollahassani, A., Heshmati, A.A. and Rashed, A. (2010), "Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks", J. Plant Nut. Soil Sci., 173(3), 368-379. https://doi.org/10.1002/jpln.200800233.
  6. Al–Dabbous, A.N., Kumar, P. and Khan, A.R. (2017), "Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network", Atm. Pollution Res., 8(3), 446-454. https://doi.org/10.1016/j.apr.2016.11.004.
  7. An, C., Sun, C., Li, N., Huang, B., Jiang, J., Shen, Y. and Wang, Y. (2022), "Nanomaterials and nanotechnology for the delivery of agrochemicals: strategies towards sustainable agriculture", J. Nanobiotechnology, 20(1), 1-19. https://doi.org/10.1186/s12951-021-01214-7.
  8. Ashok, M., Parande, A.K. and Jayabalan, P. (2017), "Strength and durability study on cement mortar containing nano materials", Adv. Nano Res., 5(2), 99-111. https://doi.org/10.12989/anr.2017.5.2.099.
  9. Azarafza, M., Asghari-Kaljahi, E. and Moshrefy-far, M.R. (2015), "Effects of clay nanoparticles added to the Bonab landfill soil to reduce the permeability and control of leachate," Iran. J. Environ. Geol., 8(26), 7-18.
  10. Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P.M. and Derakhshani, R. (2021), "Deep learning-based landslide susceptibility mapping", Sci. Rep., 11(1), 24112. https://doi.org/10.1038/s41598-021-03585-1.
  11. Azarafza, M., HajialilueBonab, M. and Derakhshani, R. (2022), "A deep learning method for the prediction of the index mechanical properties and strength parameters of marlstone", Materials, 15(19), 6899. https://doi.org/10.3390/ma15196899.
  12. Azizpour, A., Azarafza, M. and Akgun, H. (2020), "The impact of municipal waste disposal of heavy metals on environmental pollution: A case study for Tonekabon, Iran", Adv. Environ. Res., 9(3), 175-89. https://doi.org/10.12989/aer.2020.9.3.175.
  13. Baghbani, A., Choudhury, T., Costa, S. and Reiner, J. (2022), "Application of artificial intelligence in geotechnical engineering: A state-of-the-art review", Earth Sci. Rev., 228, 103991. https://doi.org/10.1016/j.earscirev.2022.103991.
  14. Basma, A.A., Barakat, S.A. and Omar, M. (2003). "Modeling time dependent swell of clays using sequential artificial neural networks", Environ. Eng. Geosci.,9(3), 279-288. https://doi.org/10.2113/9.3.279.
  15. Beavis, R.C., Colby, S.M., Goodacre, R., Harrington, P.D.B., Reilly, J.P., Sokolow, S. and Wilkerson, C.W. (2000), "Artificial intelligence and expert systems in mass spectrometry", Encyclopedia Anal. Chem., 11, 558. https://doi.org/10.1002/9780470027318.a6002
  16. Behrens, T., Förster, H., Scholten, T., Steinrücken, U., Spies, E.D. and Goldschmitt, M. (2005), "Digital soil mapping using artificial neural networks", J. Plant Nut. Soil Sci., 168(1), 21-33. https://doi.org/10.1002/jpln.200421414.
  17. Beiranvand, B. and Rajaee, T. (2022), "Application of artificial intelligence-based single and hybrid models in predicting seepage and pore water pressure of dams: A state-of-the-art review", Adv. Eng. Softw., 173, 103268. https://doi.org/10.1016/j.advengsoft.2022.103268.
  18. Besalatpour, A., Hajabbasi, M.A., Ayoubi, S., Afyuni, M., Jalalian, A. and Schulin, R.J.S.S. (2012), "Soil shear strength prediction using intelligent systems: Artificial neural networks and an adaptive neuro-fuzzy inference system", Soil Sci. Plant Nut., 58(2), 149-160. https://doi.org/10.1080/00380768.2012.661078.
  19. Branchu, S., Rogueda, P.G., Plumb, A.P. and Cook, W.G. (2007), "A decision-support tool for the formulation of orally active, poorly soluble compounds", Eur. J. Pharm. Sci., 32(2), 128-139. https://doi.org/10.1016/j.ejps.2007.06.005.
  20. Brousset, J., Pehovaz, H., Quispe, G., Raymundo, C. and Moguerza, J.M. (2023), "Rock mass classification method applying neural networks to minimize geomechanical characterization in underground Peruvian mines", Energy Rep., 9, 376-386. https://doi.org/10.1016/j.egyr.2023.05.246.
  21. Buszewski, B. and Kowalkowski, T. (2006), "A new model of heavy metal transport in the soil using nonlinear artificial neural networks", Environ. Eng. Sci., 23(4), 589-595. https://doi.org/10.1089/ees.2006.23.589.
  22. Caglar, N. and Arman, H. (2007), "The applicability of neural networks in the determination of soil profiles", Bull. Eng. Geol. Environ., 66, 295-301. https://doi.org/10.1007/s10064-006-0075-9.
  23. Calderano Filho, B., Polivanov, H., Chagas, C.D.S., Carvalho Júnior, W.D., Barroso, E.V., Guerra, A.J.T. and Calderano, S.B. (2014), "Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹", Revista Brasileira de Ciência do Solo, 38, 1681-1693. https://doi.org/10.1590/S0100-06832014000600003
  24. Chaudhry, N., Dwivedi, S., Chaudhry, V., Singh, A., Saquib, Q., Azam, A. and Musarrat, J. (2018), "Bio- inspired nanomaterials in agriculture and food: Current status, foreseen applications and challenges", Microbial Pathogen., 123, 196-200. https://doi.org/10.1016/j.micpath.2018.07.013.
  25. Chithra, S., Kumar, S.S., Chinnaraju, K. and Ashmita, F.A. (2016), "A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks", Constr. Build. Mater., 114, 528-535. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
  26. Chowdhury, S., Yusof, F., Sulaiman, N., Sidek, S.N. and Faruck, M.O. (2016), "A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production", ARPN J. Eng. Appl. Sci., 11, 12206-12211.
  27. Coll, C., Notter, D., Gottschalk, F., Sun, T., Som, C. and Nowack, B. (2016), "Probabilistic environmental risk assessment of five nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes)", Nanotoxicology, 10(4), 436-444. https://doi.org/10.3109/17435390.2015.1073812.
  28. Derakhshanfard, F. and Mehralizadeh, A. (2018), "Application of artificial neural networks for viscosity of crude oil-based nanofluids containing oxides nanoparticles", J. Petrol. Sci. Eng., 168, 263-272. https://doi.org/10.1016/j.petrol.2018.05.018.
  29. Dikshit, A., Pradhan, B. and Alamri, A.M. (2021), "Pathways and challenges of the application of artificial intelligence to geohazards modelling", Gondwana Res., 100, 290-301. https://doi.org/10.1016/j.gr.2020.08.007.
  30. Dugan, N. and Erkoç, Ş. (2009), "Genetic algorithms in application to the geometry optimization of nanoparticles", Algorithms, 2(1), 410-428. https://doi.org/10.3390/a2010410.
  31. Ebid, A.M. (2021), "35 Years of (AI) in geotechnical engineering: State of the art", Geotech. Geol. Eng., 39(2), 637-690. https://doi.org/10.1007/s10706-020-01536-7.
  32. Ebid, A.M., Nwobia, L.I., Onyelowe, K.C. and Aneke, F.I. (2021), "Predicting nanobinder-improved unsaturated soil consistency limits using genetic programming and artificial neural networks", Appl. Comput. Intel. Soft Comput., 2021, 5992628. https://doi.org/10.1155/2021/5992628.
  33. El-Sayed, A.A., Fathy, I.N., Tayeh, B.A. and Almeshal, I. (2022), "Using artificial neural networks for predicting mechanical and radiation shielding properties of different nano-concretes exposed to elevated temperature", Constr. Build. Mater., 324, 126663. https://doi.org/10.1016/j.conbuildmat.2022.126663.
  34. Esmaeilzadeh-Gharedaghi, E., Faramarzi, M.A., Amini, M.A., Rouholamini Najafabadi, A., Rezayat, S.M. and Amani, A. (2012), "Effects of processing parameters on particle size of ultrasound prepared chitosan nanoparticles: An artificial neural networks study", Pharm. Develop. Technol., 17(5), 638-647. https://doi.org/10.3109/10837450.2012.696269.
  35. Ferhat, U. and Nida, K. (2021), "Artificial intelligence sense in nanotechnology", Proceedings of the 8th International Conference on Materials Science and Nano-technology for Next Generation, Rome, Italy, December.
  36. Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S.Y., Mumper, R.J. and Tropsha, A. (2010), "Quantitative nanostructure−activity relationship modeling", ACS Nano, 4(10), 5703-5712. https://doi.org/10.1021/nn1013484.
  37. George, A. and Kannan, K. (2020), "Investigation on the geotechnical properties of nano clay treated clayey soil", Int. J. Res. Eng. Sci. Manag., 3(2), 453-5.
  38. Gottschalk, F., Kost, E. and Nowack, B. (2013), "Engineered nanomaterials in water and soils: A risk quantification based on probabilistic exposure and effect modeling", Environ. Toxicol. Chem., 32(6), 1278-1287. https://doi.org/10.1002/etc.2177.
  39. Gunaydin, O., Gokoglu, A. and Fener, M. (2010), "Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks", Adv. Eng. Softw., 41(9), 1115-1123. https://doi.org/10.1016/j.advengsoft.2010.06.008.
  40. Gupta, S. (2013), "Using artificial neural network to predict the compressive strength of concrete containing nano-silica", Civil Eng. Architect., 1(3), 96-102. https://doi.org/10.13189/cea.2013.010306.
  41. Harsh, H., Moghal, A.A.B., Rasheed, R.M. and Almajed, A. (2023), "State-of-the-art review on the role and applicability of select nano-compounds in geotechnical and geoenvironmental applications", Arab. J. Sci. Eng., 48(4), 4149-4173. https://doi.org/10.1007/s13369-022-07036-5.
  42. Hussien, R.S. and Albusoda, B.S. (2023), "Effect of permeation grouting with nano-materials on shear strength of sandy soil: An experimental study", AIP Conf. Proc., 2651, 020024. https://doi.org/10.1063/5.0132680.
  43. Ioannou, A., Gohari, G., Papaphilippou, P., Panahirad, S., Akbari, A., Dadpour, M.R. and Fotopoulos, V. (2020), "Advanced nanomaterials in agriculture under a changing climate: The way to the future?", Environ. Experim. Botany, 176, 104048. https://doi.org/10.1016/j.envexpbot.2020.104048.
  44. Isik, F. and Ozden, G. (2013), "Estimating compaction parameters of fine-and coarse-grained soils by means of artificial neural networks", Environ. Earth Sci., 69, 2287-2297. https://doi.org/10.1007/s12665-012-2057-5.
  45. Itani, O.M. and Najjar, Y.M. (2000), "Three-dimensional modeling of spatial soil properties via artificial neural networks", Transport. Res. Rec., 1709(1), 50-59. https://doi.org/10.3141/1709-07.
  46. JafarianBarough, M., Ç elik, S. and Oltulu, M. (2022), "Investigation into the Effect of Nanomaterial Injection on Improving the Geotechnical Properties of Granular Soils", Iran. J. Sci. Technol., Trans. Civil Eng., 46(4), 3163-3179. https://doi.org/10.1007/s40996-021-00785-7.
  47. Jaffar, S.T.A., Chen, X., Bao, X., Raja, M.N.A., Abdoun, T. and El-Sekelly, W. (2024), "Data-driven intelligent modeling of unconfined compressive strength of heavy metal-contaminated soil", J. Rock Mech. Geotech. Eng., In Press. https://doi.org/10.1016/j.jrmge.2024.05.025.
  48. Jain, K. (2008), The Handbook of Nanomedicine (1st Edition). Totowa, New Jersey, U.S.A.
  49. Jeremiah, J.J., Abbey, S.J., Booth, C.A. and Kashyap, A. (2021), "Results of application of artificial neural networks in predicting geo-mechanical properties of stabilised clays—a review", Geotechnics, 1(1), 147-171. https://doi.org/10.3390/geotechnics1010008.
  50. Johnston, R.L. (2003), "Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries", Dalton Transact., 22, 4193-4207. https://doi.org/10.1039/B305686D.
  51. Jong, S.C., Ong, D.E.L. and Oh, E. (2021), "State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction", Tunnel. Undergr. Sp. Technol., 113, 103946. https://doi.org/10.1016/j.tust.2021.103946.
  52. Kaloop, M.R., Elrahman, M.A. and Hu, J.W. (2022), "Nondestructive tests for defections detection of nanoparticles in cement-based materials: A review", Adv. Nano Res., 12(1), 1-23. https://doi.org/10.12989/anr.2022.12.1.001.
  53. Kartam, N., Flood, I. and Garrett, J.H. (1997), Artificial Neural Networks for Civil Engineers: Fundamentals and Applications. ASCE press, Reston, Virginia, U.S.A.
  54. Khanlari, G.R., Heidari, M., Momeni, A.A. and Abdilor, Y. (2012), "Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods", Eng. Geol., 131, 11-18. https://doi.org/10.1016/j.enggeo.2011.12.006.
  55. Khataee, A.R. (2009), "Photocatalytic removal of CI Basic Red 46 on immobilized TiO2 nanoparticles: Artificial neural network modelling", Environ. Technol., 30(11), 1155-1168. https://doi.org/10.1080/09593330903133911.
  56. Khataee, A.R. and Kasiri, M.B. (2010), "Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis", J. Molecular Catal. A Chem., 331(1-2), 86-100. https://doi.org/10.1016/j.molcata.2010.07.016.
  57. Khayati, G.R. (2020), "A predictive model on size of silver nanoparticles prepared by green synthesis method using hybrid artificial neural network-particle swarm optimization algorithm", Measurement, 151, 107199. https://doi.org/10.1016/j.measurement.2019.107199.
  58. Khodabandeh, M.A., Nagy, G. and Török, Á . (2023), "Stabilization of collapsible soils with nanomaterials, fibers, polymers, industrial waste, and microbes: Current trends", Construct. Build. Mater., 368, 130463. https://doi.org/10.1016/j.conbuildmat.2023.130463.
  59. Lee, S.J., Lee, S.R. and Kim, Y.S. (2003), "An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation", Comput. Geotech., 30(6), 489-503. https://doi.org/10.1016/S0266-352X(03)00058-2.
  60. Levine, E.R., Kimes, D.S. and Sigillito, V.G. (1996), "Classifying soil structure using neural networks", Ecol. Model., 92(1), 101-108. https://doi.org/10.1016/0304-3800(95)00199-9.
  61. Liu, Q., Peng, K., Zeng, J., Marzouki, R., Majdi, A., Jan, A. and Assilzadeh, H. (2022), "Effects of mining activities on Nanosoil management using artificial intelligence models of ANN and ELM", Adv. Nano Res., 12(6), 549-566. https://doi.org/10.12989/anr.2022.12.6.549.
  62. Majeed, Z.H. and Taha, M.R. (2012), "Effect of nanomaterial treatment on geotechnical properties of a Penang soft soil", J. Asian Sci. Res., 2(11), 587.
  63. Mansour, Y., Battie, Y., Naciri, A.E. and Chaoui, N. (2019), "Artificial neural network for the classification of nanoparticles shape distributions", Opt. Lett., 44(13), 3390-3393. https://doi.org/10.1364/OL.44.003390.
  64. Marashi, M., Torkashvand, A.M., Ahmadi, A. and Esfandyari, M. (2017), "Estimation of soil aggregate stability indices using artificial neural network and multiple linear regression models", SJSS Spanish J. Soil Sci., 7(2), 122-132. https://doi.org/10.3232/SJSS.2017.V7.N2.04
  65. Meng, J., Zhong, R., Wu, Z. and Dong, M. (2022), "On the application of artificial intelligence in acute myeloid leukemia therapy", Adv. Nano Res., 13(2), 175-186. https://doi.org/10.12989/anr.2022.13.2.175.
  66. Mohammadi, M., Rajabi, A.M. and Khodaparast, M. (2022), "Experimental and numerical evaluation of the effect of nano calcium carbonate on geotechnical properties of clayey sand soil", KSCE J. Civil Eng., 26(1), 35-46. https://doi.org/10.1007/s12205-021-1914-8.
  67. Mohammed, J.H. and Hamad, A.J. (2014), "Materials, properties and application review of Lightweight concrete", Technical Review of the Faculty of Engineering University of Zulia, 37(2), 10-15.
  68. Motahar, S. and Sadri, S. (2021), "Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles", Int. J. Energy Res., 45(10), 15092-15109. https://doi.org/10.1002/er.6785.
  69. Nasr, D., Behforouz, B., Borujeni, P.R., Borujeni, S.A. and Zehtab, B. (2019), "Effect of nano-silica on mechanical properties and durability of self-compacting mortar containing natural zeolite: Experimental investigations and artificial neural network modeling" Constr. Build. Mater., 229, 116888. https://doi.org/10.1016/j.conbuildmat.2019.116888.
  70. Nasution, A., Imran, I. and Abdullah, M. (2015), "Improvement of concrete durability by nanomaterials", Proc. Eng., 125, 608-612. https://doi.org/10.1016/j.proeng.2015.11.078.
  71. Nath, F., Asish, S.M., Ganta, D., Debi, H.R., Aguirre, G. and Aguirre, E. (2022), "Artificial intelligence model in predicting geomechanical properties for shale formation: A field case in permian basin", Energies, 15(22), 8752. https://doi.org/10.3390/en15228752.
  72. Nezhad, M.R.H., Tashkhourian, J., Khodaveisi, J. and Khoshi, M.R. (2010), "Simultaneous colorimetric determination of dopamine and ascorbic acid based on the surface plasmon resonance band of colloidal silver nanoparticles using artificial neural networks", Analytical Methods, 2(9), 1263-1269. https://doi.org/10.1039/C0AY00302F.
  73. Nikbakht, M., BehroozSarand, F., Dabiri, R. amdBonab, M.H. (2022), "Application of nanoclay and nanofiber filters to reduce soil permeability and leachates from landfill liners: A Review", Geotech. Geol., 18(1), 671-680. https://doi.org/10.30495/GEOTECH.2022.693134.
  74. Norhasri, M.M., Hamidah, M.S. and Fadzil, A.M. (2017), "Applications of using nano material in concrete: A review", Constr. Build. Mater., 133, 91-97. https://doi.org/10.1016/j.conbuildmat.2016.12.005.
  75. Onyelowe, K.C., Mojtahedi, F.F., Ebid, A.M., Rezaei, A., Osinubi, K.J., Eberemu, A.O. and Rehman, Z.U. (2023), "Selected AI optimization techniques and applications in geotechnical engineering", Cogent Eng., 10(1), 2153419. https://doi.org/10.3390/app13106074.
  76. Pachepsky, Y.A., Timlin, D. and Varallyay, G.Y. (1996), "Artificial neural networks to estimate soil water retention from easily measurable data", Soil Sci. Soc. Am. J., 60(3), 727-733. https://doi.org/10.2136/sssaj1996.03615995006000030007x.
  77. Pham, B.T., Nguyen, M.D., Ly, H.B., Pham, T.A., Hoang, V., Van Le, H. and Bui, G.L. (2020), "Development of artificial neural networks for prediction of compression coefficient of soft soil", Proceedings of the 5th International Conference on Geotechnics, Civil Engineering Works and Structures Springer Singapore, 1167-1172. https://doi.org/10.1007/978-981-15-0802-8_187.
  78. Pham, B.T., Singh, S.K. and Ly, H.B. (2020), "Using Artificial Neural Network (ANN) for prediction of soil", Vietnam J. Earth Sci., 42(4), 311-319. https://doi.org/10.15625/0866-7187/42/7/15008.
  79. Preeti, N. (2016), "Green chemistry for nanotechnology: Opportunities and future challenges", J. Res. Rev. J. Chem., 5(1), 3-4.
  80. Raja, M.N.A. and Shukla, S.K. (2021), "Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique. Geotext. Geomem., 49(5), 1280-1293. https://doi.org/10.1016/j.geotexmem.2021.04.007.
  81. Raja, M.N.A., Abdoun, T. and El-Sekelly, W. (2024a), "Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations", Buildings, 14(4), 954. https://doi.org/10.3390/buildings14040954.
  82. Raja, M.N.A., Abdoun, T. and El-Sekelly, W. (2024b), "Smart prediction of liquefaction-induced lateral spreading", J. Rock Mech. Geotech. Eng., 16(6), 2310-2325. https://doi.org/10.1016/j.jrmge.2023.05.017.
  83. Raja, M.N.A., Jaffar, S.T.A., Bardhan, A. and Shukla, S.K. (2023), "Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling", J. Rock Mech. Geotech. Eng., 15(3), 773-788. https://doi.org/10.1016/j.jrmge.2022.04.012.
  84. Rani, C.H.S., Kumar, V.P. and Togati, V.K. (2013), "Artificial neural networks (ANNS) for prediction of engineering properties of soils", Int. J. Innov. Technol. Explor. Eng., 3(1), 123-130.
  85. Ren, W., Wu, X. and Cai, R. (2022), "A hybrid artificial intelligence and IOT for investigation dynamic modeling of nano-system", Adv. Nano Res., 13(2), 165-174. https://doi.org/10.12989/anr.2022.13.2.165.
  86. Rizkalla, N. and Hildgen, P. (2005), "Artificial neural networks: Comparison of two programs for modeling a process of nanoparticle preparation", Drug Develop. Ind. Pharm., 31(10), 1019-1033. https://doi.org/10.1080/03639040500306294.
  87. Sadeghzadeh, M., Maddah, H., Ahmadi, M.H., Khadang, A., Ghazvini, M., Mosavi, A. and Nabipour, N. (2020), "Prediction of thermo-physical properties of TiO2-Al2O3/water nanoparticles by using artificial neural network", Nanomaterials, 10(4), 697. https://doi.org/10.3390/nano10040697.
  88. Saraee, H.S., Jafarmadar, S., Alizadeh‐Haghighi, E. and Ashrafi, S.J. (2016), "Experimental investigation of pollution and fuel consumption on a CI engine operated on alumina nanoparticles—diesel fuel with the aid of artificial neural network", Environ. Prog. Sustain. Energy, 35(2), 540-546. https://doi.org/10.1002/ep.12233.
  89. Sarmadian, F., Mehrjardi, R.T. and Akbarzadeh, A. (2009), "Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan Province, north of Iran", Australian J. Basic Appl. Sci., 3(1), 323-329.
  90. Shabanzadeh, P., Shameli, K., Ismail, F. and Mohagheghtabar, M. (2013), "Application of artificial neural network (Ann) for the prediction of size of silver nanoparticles prepared by green method", Digest J. Nanomat. Biostruct., 8(2), 541-549.
  91. Shabanzadeh, P., Yusof, R. and Shameli, K. (2015), "Artificial neural network for modeling the size of silver nanoparticles' prepared in montmorillonite/starch bionanocomposites", J. Ind. Eng. Chem., 24, 42-50. https://doi.org/10.1016/j.jiec.2014.09.007.
  92. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2008), "State of the art of artificial neural networks in geotechnical engineering", Elect. J. Geotech. Eng., 8(1), 1-26. https://doi.org/10.5515/JKIEES.2008.8.1.001
  93. Sharma, S., Ahmed, S., Naseem, M., Alnumay, W.S., Singh, S. and Cho, G.H. (2021), "A survey on applications of artificial intelligence for pre-parametric project cost and soil shear strength estimation in construction and geotechnical engineering", Sensors, 21(2), 463. https://doi.org/10.3390/s21020463.
  94. Shekaftik, S.O., Ashtarinezhad, A., Yarahmadi, R., Rasouli, M., Soleimani, M. and HosseiniShirazi, F. (2020), "Relationship between chemical composition and physical State of used nanomaterials in nanotechnology companies with type and prevalance of symptoms of employees of these companies in Tehran, Iran", Iran Occupational Health, 17(1), 936-950.
  95. Siddig, O., Gamal, H., Elkatatny, S. and Abdulraheem, A. (2022), "Applying different artificial intelligence techniques in dynamic Poisson's ratio prediction using drilling parameters", J. Energy Res. Technol., 144(7), 073006. https://doi.org/10.1115/1.4052185.
  96. Siepmann, P., Martin, C.P., Vancea, I., Moriarty, P.J. and Krasnogor, N. (2007), "A genetic algorithm approach to probing the evolution of self-organized nanostructured systems", Nano Lett., 7(7), 1985-1990. https://doi.org/10.1021/nl070773m.
  97. Sitton, J.D., Zeinali, Y. and Story, B.A. (2017), "Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks", Constr. Build. Mater., 138, 214-221. https://doi.org/10.1016/j.conbuildmat.2017.02.006.
  98. Sun, T.Y., Gottschalk, F., Hungerbühler, K. and Nowack, B. (2014), "Comprehensive probabilistic modelling of environmental emissions of engineered nanomaterials", Environ. Pollut., 185, 69-76. https://doi.org/10.1016/j.envpol.2013.10.004.
  99. Syed, F.I., AlShamsi, A., Dahaghi, A.K. and Neghabhan, S. (2022), "Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs–A systematic literature review", Petroleum, 8(2), 158-166. https://doi.org/10.1016/j.petlm.2020.12.001.
  100. Takeuchi, I., Koinuma, H., Amis, E.J., Newsam, J.M., Wille, L.T. and Buelens, C. (2002), "SYMPOSIUM S: Combinatorial and artificial intelligence methods in materials science", Mater. Res. Soc. Symp. Proc., 700, 358-371.
  101. Taniguchi, N. (1974), "On the Basic Concept of 'Nano Technology'," Proceedings of the International Conference on Production Engineering, Tokyo, Japan, August.
  102. Tavakoli, H.R., Omran, O.L., Shiade, M.F. and Kutanaei, S.S. (2014), "Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network", Latin Am. J. Solids Struct., 11, 1906-1923. https://doi.org/10.1590/S1679-78252014001100002.
  103. Turk, G., Logar, J. and Majes, B. (2001), "Modelling soil behaviour in uniaxial strain conditions by neural networks", Adv. Eng. Softw., 32(10-11), 805-812. https://doi.org/10.1016/S0965-9978(01)00032-1.
  104. Utsev, T., Tiza, T.M., Mogbo, O., Singh, S.K., Chakravarti, A., Shaik, N. and Singh, S.P. (2022), "Application of nanomaterials in civil engineering", Mater. Today Proc., 62, 5140-5146. https://doi.org/10.1016/j.matpr.2022.02.480.
  105. Wang, Y., Kalinina, A., Sun, T. and Nowack, B. (2016), "Probabilistic modeling of the flows and environmental risks of nano-silica", Sci. Total Environ., 545, 67-76. https://doi.org/10.1016/j.scitotenv.2015.12.100.
  106. Xiang, Q., Yu, H., Chu, H., Hu, M., Xu, T., Xu, X. and He, Z. (2022), "The potential ecological risk assessment of soil heavy metals using self-organizing map", Sci. Total Environ., 843, 156978. https://doi.org/10.1016/j.scitotenv.2022.156978.
  107. Yanzhen, Q., Zandi, Y., Rahimi, A., Pourkhorshidi, S., Roco Videla, A., Khadimallah, M.A. and Assilzadeh, H. (2021), "Nano-SiO2 for efficiency of geotechnical properties of fine soils in mining and civil engineering", Adv. Nano Res., 11(3), 301-312. https://doi.org/10.12989/anr.2021.11.3.301.
  108. Youshia, J., Ali, M.E. and Lamprecht, A. (2017), "Artificial neural network based particle size prediction of polymeric nanoparticles", Eur. J. Pharm. Biopharm., 119, 333-342. https://doi.org/10.1016/j.ejpb.2017.06.030.
  109. Zahmatkesh, S., Hajiaghaei-Keshteli, M., Bokhari, A., Sundaramurthy, S., Panneerselvam, B. and Rezakhani, Y. (2022), "Wastewater treatment with nanomaterials for the future: A state-of-the-art review", Environ. Res., 114652. https://doi.org/10.1016/j.envres.2022.114652.
  110. Zhai, Y., Thomasson, J.A., Boggess III, J.E. and Sui, R. (2006), "Soil texture classification with artificial neural networks operating on remote sensing data", Comput. Electr. Agricul., 54(2), 53-68. https://doi.org/10.1016/j.compag.2006.08.001.
  111. Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A. and Lynch, I. (2021), "Nanotechnology and artificial intelligence to enable sustainable and precision agriculture", Nature Plants, 7(7), 864-876. https://doi.org/10.1038/s41477-021-00946-6.
  112. Zheng, W. and Tropsha, A. (2000), "Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle", J. Chem. Inform. Comput. Sci., 40(1), 185-194. https://doi.org/10.1021/ci980033m.
  113. Zhu, G., Wu, X., Ge, J., Liu, F., Zhao, W. and Wu, C. (2020), "Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a self-organizing map (SOM)", J. Clean. Prods., 257, 120664. https://doi.org/10.1016/j.jclepro.2020.120664.