Prediction of Gypseous Soil Settlement Using Artificial Neural Network (ANN)
Keywords:
Gypseous soil, Artificial Neural Network, SettlementAbstract
Gypseous soil exhibits problematic geotechnical engineering properties as they expand, collapse, disperse, undergo excessive settlement, owns a distinct lack of strength, and it is soluble. Gypseous soil has a metastable structure, with dissolvable minerals with a minimal quantity of clay binding the particles together. When gypseous soil unsaturated, they are quite potent. When they are subjected to increased wetness, however, the excess water weakens or damages the bonds, resulting in shear failure and subsequent settlement. Estimating the settlement of shallow foundations on gypseous soils is a difficult topic that is still not fully understood. It is concluded that artificial neural network (ANN) is appeared to be viable solution since it has been successfully used in numerous prognosis applications in geotechnical engineering. In this research, the precipitation values of gypsum soil were predicted under the influence of the applied load using an artificial neural network. The study found that this model is very good in predicting precipitation and found a convergence between the real values and the predict values.
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References
A. KI, "Effect of gypsum on the hydro-mechanical characteristics of partially saturated sandy soil " Ph.D. thesis, Geoenvironmental Research Centre, Cardiff School of Engineering, UK, Cardiff University, 2019.
A. Ahmed and K. Ugai, "Environmental effects on durability of soil stabilized with recycled gypsum," Cold regions science and technology, vol. 66, no. 2-3, pp. 84-92, 2011. DOI: https://doi.org/10.1016/j.coldregions.2010.12.004
B. J. Nareeman, "A study on the scale effect on bearing capacity and settlement of shallow foundations," International Journal of Engineering and Technology, vol. 2, no. 3, pp. 480-488, 2012.
B. Tarawneh, T. Masada, and S. Sargand, "Estimated and measured settlements of shallow foundation supporting bridge substructure," Jordan Journal of Civil Engineering, vol. 7, no. 2, pp. 224-235, 2013.
H. MolaAbasi, M. Saberian, A. Khajeh, J. Li, and R. J. Chenari, "Settlement predictions of shallow foundations for non-cohesive soils based on CPT records-polynomial model," Computers and Geotechnics, vol. 128, p. 103811, 2020. DOI: https://doi.org/10.1016/j.compgeo.2020.103811
A. J. Maren, C. T. Harston, and R. M. Pap, Handbook of neural computing applications. Academic Press, 2014.
W. Zhang and A. T. Goh, "Multivariate adaptive regression splines and neural network models for prediction of pile drivability," Geoscience Frontiers, vol. 7, no. 1, pp. 45-52, 2016. DOI: https://doi.org/10.1016/j.gsf.2014.10.003
W. Zhang and A. T. C. Goh, "Reliability analysis of geotechnical infrastructures," Geoscience Frontiers, vol. 9, p. 1595e1596, 2018. DOI: https://doi.org/10.1016/j.gsf.2018.01.001
W. Zhang, R. Zhang, W. Wang, F. Zhang, and A. T. C. Goh, "A multivariate adaptive regression splines model for determining horizontal wall deflection envelope for braced excavations in clays," Tunnelling and Underground Space Technology, vol. 84, pp. 461-471, 2019. DOI: https://doi.org/10.1016/j.tust.2018.11.046
A. T. Goh, W. Zhang, and K. Wong, "Deterministic and reliability analysis of basal heave stability for excavation in spatial variable soils," Computers and Geotechnics, vol. 108, pp. 152-160, 2019. DOI: https://doi.org/10.1016/j.compgeo.2018.12.015
W. Zhang et al., "State-of-the-art review of soft computing applications in underground excavations," Geoscience Frontiers, vol. 11, no. 4, pp. 1095-1106, 2020. DOI: https://doi.org/10.1016/j.gsf.2019.12.003
R. B. Kaunda, R. B. Chase, A. E. Kehew, K. Kaugars, and J. P. Selegean, "Neural network modeling applications in active slope stability problems," Environmental Earth Sciences, vol. 60, no. 7, pp. 1545-1558, 2010. DOI: https://doi.org/10.1007/s12665-009-0290-3
Y. Yukselen and Y. Erzin, "Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations," Environmental Geology, vol. 54, no. 5, pp. 1059-1066, 2008. DOI: https://doi.org/10.1007/s00254-007-0872-x
A. Ardakani and A. Kordnaeij, "Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm," European Journal of Environmental and Civil Engineering, vol. 23, no. 4, pp. 449-462, 2019. DOI: https://doi.org/10.1080/19648189.2017.1304269
Dawood, "strength stiffness and compressibility of understanding gypseous soil during wetting and soaking," civil, University of Technology, University of Technology, 2016.
Q. A. Mehdi, "The Effect of Sand Particles Size on Bearing Capacity Factors of Shallow Footings ", civil, University of Technology, University of Technology, 2005.
R. Salman, "Bearing capacity of shallow footing on compacted filling dune sand over reinforced gypseous soil," Journal of Engineering, vol. 19, no. 5, pp. 532-542, 2013.
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