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Literature Review in Artificial Neural Network for the Strength Calculation of Soil

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Recent Trends in Materials

Part of the book series: Springer Proceedings in Materials ((SPM,volume 18))

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Abstract

The soil properties are considered to be the important parameter related to the soil. There are different factors associated with it so that the proper results can be generated. Shear strength is considered to be the resistance internally present and applied for the forces which are external in nature. The other parameters are also involved for the calculation of the strength of soil. It is determined in laboratory using sophisticated equipment and is found to be associated with errors. Alternative such as the empirical correlations are considered to be valid for particular range. With the advent of computational technics, mathematical, numerical and artificial intelligence are explored for determination of shear strength. The artificial neural network is the technique where the prediction of the different soil properties is to be obtained. The properly collected data of different soil samples are then given as input to ANN Technique. It is very much necessary to predict the strength of the soil as there are numerous applications including construction, testing, sports, agriculture, military, etc. The different parameters are to be considered for the proper relationship to be obtained. The present paper is related to the review which has been carried out on the use of the artificial neural network in the strength calculation of the soil.

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References

  1. Abu-Farsakh MY, Mojumder MAH (2020) Exploring artificial neural network to evaluate the undrained shear strength of soil from cone penetration test data. Transp Res Rec 2674(4):11–22

    Article  Google Scholar 

  2. Dungca JR, Galupino JG (2017) Artificial neural network permeability modeling of soil blended with fly ash. Geomate J 12(31):77–82

    Google Scholar 

  3. Erzin Y, Rao BH, Patel A, Gumaste SD, Singh DN (2010) Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity. Int J Therm Sci 49(1):118–130

    Google Scholar 

  4. Erzin Y, Gumaste SD, Gupta AK, Singh DN (2009) Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils. Can Geotech J 46(8):955–968

    Google Scholar 

  5. Ferreira EC, Milori DMBP, Ferreira EJ, Da Silva RM, Martin-Neto L (2008) Artificial neural network for Cu quantitative determination in soil using a portable laser induced breakdown spectroscopy system. Spectrochimica Acta Part B: Atomic Spectrosc 63(10):1216–1220

    Google Scholar 

  6. Hanandeh S, Ardah A, Abu-Farsakh M (2020) Using artificial neural network and genetics algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula. Transp Geotech 24:100358

    Google Scholar 

  7. Kalantary F, Kordnaeij A (2012) Prediction of compression index using artificial neural network. Scien Res Essays 7(31):2835–2848

    Google Scholar 

  8. Keshavarzi A, Sarmadian F, Omran ESE, Iqbal M (2015) A neural network model for estimating soil phosphorus using terrain analysis. The Egyptian J Rem Sens Space Sci 18(2):127–135

    Google Scholar 

  9. Kiran S, Lal B (2016) Modelling of soil shear strength using neural network approach. Electron J Geotech Eng 21(10):3751–3771

    Google Scholar 

  10. Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comp Geotech 69:291–300

    Google Scholar 

  11. Bunyamin SA, Ijimdiya TS, Eberemu AO, Kolawole JO (2018) Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust. J Soft Comput Civ Eng 2(3):50–71

    Google Scholar 

  12. Ozturk M, Salman O, Koc M (2011) Artificial neural network model for estimating the soil temperature. Can J Soil Sci 91(4):551–562

    Article  Google Scholar 

  13. Sungheetha A, Sharma R (2021) Fuzzy chaos whale optimization and BAT integrated algorithm for parameter estimation in sewage treatment. J Soft Comput Paradigm (JSCP) 3(01):10–18

    Article  Google Scholar 

  14. Lin C-J, Nan-Jing W (2021) An ANN model for predicting the compressive strength of concrete. Appl Sci 11(9):3798

    Google Scholar 

  15. Karthigaikumar P (2021) Industrial quality prediction system through data mining algorithm. J Electron Inf 3(2):126–137

    Google Scholar 

  16. Pratibha C, Reddy KM, Bharathi L, Manasa M, Gandhiraj R (2019) Simulation of dual polarization radar for rainfall parameter and drop size distribution estimation. In: International conference on ıntelligent computing, ınformation and control systems. Springer, Cham, pp 424–433

    Google Scholar 

  17. Salahudeen AB, Sadeeq JA, Badamasi A, Onyelowe KC (2020) Prediction of unconfined compressive strength of treated expansive clay using back-propagation artificial neural networks. Nigerian J Eng 27(1)

    Google Scholar 

  18. Priyadarshee A, Chandra S, Gupta D, Kumar V (2020) Neural models for unconfined compressive strength of Kaolin clay mixed with pond ash, rice husk ash and cement. J Soft Comput Civ Eng 4(2):85–102

    Google Scholar 

  19. Ali B, Rahman MA, Rafizul IM (2016) Prediction of California bearing ratio of stabilized soil using artificial neural network. In: Proceedings of the 3rd ınternational conference on civil engineering for sustainable development (ICCESD 2016)

    Google Scholar 

  20. Salahudeen AB, Sadeeq JA (2019) California bearing ratio prediction of modified black clay using artificial neural networks. In: Book of proceedings, West Africa built environment research (WABER) conference, Accra, Ghana, pp 268–281

    Google Scholar 

  21. Vakili AH, Davoodi S, Arab A, Selamat MR (2015) Use of artificial neural network in predicting permeability of dispersive clay treated with lime and pozzolan. IJSRES 3(1):23–37

    Google Scholar 

  22. Pham BT, Nguyen MD, Bui KTT, Prakash I, Chapi K, Bui DT (2019) A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena 173:302–311

    Google Scholar 

  23. Rao RMP, Rao HS (2012) Prediction of compressive strength of concrete with different aggregate binder ratio using ANN model. Int J Eng Res Technol 1(10):1–10

    Google Scholar 

  24. Park HI, Kim YT (2011) Prediction of strength of reinforced lightweight soil using an artificial neural network. Eng Comput

    Google Scholar 

  25. Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Australian Geomech 36(1):49–62

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Prof Ram Meghe College of Engineering & Management, Badnera, Amravati, India

    Rahul Ramdas Wankhade

  2. Department of Civil Engineering, Mauli College of Engineering, Shegaon, India

    P. V. Durge

Authors
  1. Rahul Ramdas Wankhade
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  2. P. V. Durge
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Corresponding author

Correspondence to Rahul Ramdas Wankhade .

Editor information

Editors and Affiliations

  1. JCT College of Engineering and Technology, Coimbatore, India

    K. Geetha

  2. Department of Electrical Power Engineering, University of South-Eastern Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Department of Industrial and Systems Engineering, Chung Yuan Christian University (CYCU), Taoyuan City, Taiwan

    Hui-Ming Wee

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Wankhade, R.R., Durge, P.V. (2022). Literature Review in Artificial Neural Network for the Strength Calculation of Soil. In: Geetha, K., Gonzalez-Longatt, F.M., Wee, HM. (eds) Recent Trends in Materials. Springer Proceedings in Materials, vol 18. Springer, Singapore. https://doi.org/10.1007/978-981-19-5395-8_11

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  • DOI: https://doi.org/10.1007/978-981-19-5395-8_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5394-1

  • Online ISBN: 978-981-19-5395-8

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

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