Advertisement

Developing artificial neural network models to predict allowable bearing capacity and elastic settlement of shallow foundation in Sharjah, United Arab Emirates

  • Maher Omar
  • Khaled Hamad
  • Mey Al Suwaidi
  • Abdallah Shanableh
Original Paper
  • 54 Downloads

Abstract

This research proposes the use of artificial neural network to predict the allowable bearing capacity and elastic settlement of shallow foundation on granular soils in Sharjah, United Arab Emirates. Data obtained from existing soil reports of 600 boreholes were used to train and validate the model. Three parameters (footing width, effective unit weight, and SPT blow count) are considered to have the most significant impact on the magnitude of allowable bearing capacity and elastic settlement of shallow foundations, and thus were used as the model inputs. Throughout the study, depth of footing was limited to 1.5 m below existing ground level and water table depth taken at the level of the footing. Performance comparison of the developed models (in terms of coefficient of determination, root mean square error, and mean absolute error) revealed that the developed artificial neural network models could be effectively used for predicting the allowable bearing capacity and elastic settlement. As such, the developed models can be used at the preliminary stage of estimating the allowable bearing capacity and settlements of shallow foundations on granular soils, instead of the conventional methods.

Keywords

Shallow foundations Allowable bearing capacity Elastic settlement Artificial neural network Granular soil Sharjah 

References

  1. Asadizadeh M, Hossaini MF (2016) Predicting rock mass deformation modulus by artificial intelligence approach based on dilatometer tests. Arab J Geosci 9:96.  https://doi.org/10.1007/s12517-015-2189-5 CrossRefGoogle Scholar
  2. Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181CrossRefGoogle Scholar
  3. Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36CrossRefGoogle Scholar
  4. EL Menshawy M, Benharref A, Serhani M (2015) An automatic mobile-health based approach for EEG epileptic seizures detection. Expert Systems with Applications An International Journal 42(20):7157–7174CrossRefGoogle Scholar
  5. Erzin Y, Gul TO (2014) The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test. Neural Comput & Applic 24(3–4):891–900CrossRefGoogle Scholar
  6. Gupta R, Goyal K, Yadav N (2016) Prediction of safe bearing capacity of noncohesive soil in arid zone using artificial neural networks. Int J Geomech 16(2):04015044CrossRefGoogle Scholar
  7. Hanna AM, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng 27(6):521–540CrossRefGoogle Scholar
  8. Hasanzadehshooiili H, Mahinroosta R, Lakirouhani A, Oshtaghi V (2014) Using artificial neural network (ANN) in prediction of collapse settlements of sandy gravels. Arab J Geosci 7:2303–2314.  https://doi.org/10.1007/s12517-013-0858-9 CrossRefGoogle Scholar
  9. Kiefa MAA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron 124(12):1177–1185CrossRefGoogle Scholar
  10. Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213CrossRefGoogle Scholar
  11. Marto A, Hajihassani M, Momeni E (2014) Bearing capacity of shallow foundation’s prediction through hybrid artificial neural networks. Appl Mech Mater 567:681–686CrossRefGoogle Scholar
  12. Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74(3):745–757CrossRefGoogle Scholar
  13. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131CrossRefGoogle Scholar
  14. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19(1):85–93CrossRefGoogle Scholar
  15. Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425.  https://doi.org/10.1007/s12517-009-0091-8 CrossRefGoogle Scholar
  16. Nazir R, Momeni E, Hajihassani M (2014) Prediction of spread foundations’ settlement in cohesionless soils using a hybrid particle swarm optimization-based ANN approach. In: Proc. of the Intl. Conf. on Advances in Civil, Structural And Mechanical Engineering- CSM 2014. London, United Kingdom.  https://doi.org/10.15224/978-1-63248-012-5-63
  17. Nazir et al (2015) An artificial neural network approach for prediction of bearing capacity of spread foundations in sand. Jurnal Teknologi (Sciences & Engineering) 72(3):9–14Google Scholar
  18. Ornek M, Laman M, Demir A, Yildiz A (2012) Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils Found 52(1):69–80CrossRefGoogle Scholar
  19. Padmini D, Ilamparuthi K, Sudheer KP (2008) Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Comput Geotech 35(1):33–46CrossRefGoogle Scholar
  20. Pal M, Deswal S (2008) Modeling pile capacity using support vector machines and generalized regression neural network. J Geotech Geoenviron Eng 134(7):1021–1024CrossRefGoogle Scholar
  21. Park HI (2011) Study for application of artificial neural networks in geotechnical problems. In: Hui CLP (ed) Artificial neural networks application. INTECH.  https://doi.org/10.5772/15011 Google Scholar
  22. Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5(4):318–324CrossRefGoogle Scholar
  23. Shahin M a, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenviron 128(9):785–793CrossRefGoogle Scholar
  24. Soleimanbeigi A, Hataf A (2005) Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks. Geosynth Int 12(6):321–332CrossRefGoogle Scholar
  25. Tarawneh B (2013) Pipe pile setup: database and prediction model using artificial neural network. Soils Found 53(4):607–615CrossRefGoogle Scholar
  26. Torabi-Kaveh M, Naseri F, Saneie S, Sarshari B (2015) Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arab J Geosci 8:2889–2897.  https://doi.org/10.1007/s12517-014-1331-0 CrossRefGoogle Scholar
  27. Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966CrossRefGoogle Scholar
  28. Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46(4):803–810CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Maher Omar
    • 1
  • Khaled Hamad
    • 1
  • Mey Al Suwaidi
    • 1
  • Abdallah Shanableh
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of SharjahSharjahUnited Arab Emirates

Personalised recommendations