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Research Article

CPT-based method using hybrid artificial neural network and mathematical model to predict the load-settlement behaviour of shallow foundations

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Pages 321-333 | Received 13 Sep 2019, Accepted 09 Apr 2020, Published online: 29 Apr 2020

References

  • AFNOR, 2013. NF P 94-261: justification of geotechnical work—national application standards for the implementation of Eurocode 7—shallow foundations. Paris: Association Française de Normalisation.
  • Armaghani, D.J., et al., 2017. Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications 28 (2), 391–405. https://doi.org/https://doi.org/10.1007/s00521-015-2072-z
  • Beale, M., Hagan, M., and Demuth, H., 2010. Neural Network Toolbox™ User’s Guide. Natrick: MathWorks, Inc.
  • Berardi, R., Jamiolkowski, M., and Lancellotta, R., 1991. Settlement of Shallow Foundations in Sands Selection of Stiffness on the Basis of Penetration Resistance. In: Geotechnical Engineering Congress—1991. ASCE, 185–200.
  • Bergdahl, U. and Hult, G.A.O.E., 1985. Calculation of settlements of footings in sands. SAN FRANCISCO: Balkema (AA).
  • Briaud, J.L., 2007. Spread footings in sand: load settlement curve approach. Journal of Geotechnical and Geoenvironmental Engineering, 133 (8), 905–920.
  • Briaud, J.L. and Gibbens, R., 1999. Behavior of five large spread footings in sand. Journal of Geotechnical and Geoenvironmental Engineering, 125 (9), 9. doi:https://doi.org/10.1061/(ASCE)1090-0241(1999)125:9(787)
  • Briaud, J.L. and Robert, G., 1997. Large-scale load tests and data base of spread footings on sand. United States. Federal Highway Administration.
  • Burland, J.B. and Burbridge., M.C., 1985. Settlement of foundations on sand and gravel. Proceedings of the Institution of Civil Engineers, 78 (6), 1325–1381.
  • Canépa, Y. and Depresless, D., 1990. Catalogue des essais de chargement de fondations superficielles réalisés sur sites par les LPC (1978-1990). Report FAER 1. 17. 02. 9 /8622. Melun, France: Laboratoire Régional de l’Est Parisien (LREP).
  • Chen, W., et al., 2019. Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Engineering with Computers, 1–15. https://doi.org/https://doi.org/10.1007/s00366-019-00752-x
  • Da Fonseca, A.V., FERNANDESÃ, M.M., and CARDOSOÃ, A.S., 1997. Interpretation of a footing load test on a saprolitic soil from granite. Geotechnique, 47, 3.
  • Das, B. and Sivakugan, N., 2007. Settlements of shallow foundations on granular soil—an overview. International Journal of Geotechnical Engineering, 1 (1), 19–29.
  • DeBeer, E., 1965. Bearing capacity and settlement of shallow foundation on sand. In: Proc. of Symposium held at Duke University. 15–33.
  • Dreyfus, G., et al., 2008. Apprentissage statistique: réseaux de neurones Cartes topologiques Machines à vecteurs supports. Paris: Eyrolles.
  • Eslaamizaad, S. and Robertson, P., 1996. Cone penetration test to evaluate bearing capacity of foundation in sands. In: Proceedings of the 49th Canadian Geotechnical Conference. St. John’s, Newfoundland. Richmond, BC: Canadian Geotechnical Society, 429–438.
  • Gifford, D.G., et al., 1987. Spread footings for highway bridges. McLean: FHWA.
  • Harandizadeh, H., Armaghani, D.J., and Khari, M., 2019. A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets. Engineering with Computers. https://doi.org/https://doi.org/10.1007/s00366-019-00849-3
  • Juwaied, N.S., 2018. APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN GEOTECHNICAL ENGINEERING. ARPN Journal of Engineering and Applied Sciences, 13 (8), 2764–2785.
  • Khari, M., et al., 2019. Computational estimation of lateral pile displacement in layered sand using experimental data. Measurement, 146, 110–118.
  • Long, P.D., 1993. Footings with settlement-reducing piles in non-cohesive soil. Linköping: Statens Geotekniska Institut.
  • Lutenegger, A.J. and DeGroot., D.J., 1995. Settlement of shallow foundations on granular soils. Amherst, MA: University of Massachusetts Transportation Center.
  • Martin, R.E., 1987. Settlement of residual soils. In: Foundations and Excavations in Decomposed Rock of the Piedmont Province. ASCE, 1–14.
  • Mayne, P.W. and Dasenbrock, D., 2018. Direct CPT Method for 130 Footings on Sands. Orlando. Florida: ASCE, 135–146.
  • Mayne, P.W., Uzielli, M., and Illingworth., F., 2012. Shallow footing response on sands using a direct method based on cone penetration. California: ASCE, 664–679.
  • Ménard, L. and Rousseau, J., 1962. L’évaluation des tassements, tendances nouvelles. Sols Soils, 1 (1), 13–29.
  • Meyerhof, G.G., 1956. Penetration tests and bearing capacity of cohesionless soils. Journal of the Soil Mechanics and Foundations Division, 82 (1), 1–19.
  • Momeni, E., et al., 2018. Prediction of bearing capacity of thin-walled foundation: a simulation approach. Engineering with Computers, 34, 319–327.
  • Momeni, E., et al., 2015. Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sciences Research Journal, 19, 85–93.
  • Papadopoulos, B.P., 1992. Settlements of shallow foundations on cohesionless soils. Journal of Geotechnical Engineering, 118, 3.
  • Parry, R.H.G., 1978. Estimating Foundation Settlements in Sand from Plate Bearing Tests. Geotechnique, 28 (1), 107–118.
  • Prasanth, S. and Sankar, N., 2015. Prediction of Settlement of Shallow Footings on Granular Soils using Genetic Algorithm. Asian Journal of Engineering and Technology, 3 (4), 376–383.
  • Rezania, M. and JAVADI, A.A., 2007. A new genetic programming model for predicting settlement of shallow foundations. Canadian Geotechnical Journal, 44 (12), 1462–1473.
  • Schmertmann, J.H., 1978. Guidelines for cone penetration test: performance and design. United States: Federal Highway Administration.
  • Schmertmann, J.H., Hartman, J.P., and Brown., P.R., 1978. Improved strain influence factor diagrams. Journal of Geotechnical and Geoenvironmental Engineering, 104 (8), 1131–1135.
  • Schultze, E. and Sherif, G., 1973. Prediction of settlements from evaluated settlement observations for sand. In: Proceedings Eighth International Conference on Soil Mechanics and Foundation Engineering, 1(3), Moscow: strojizdat, 225–230.
  • Shahin, M.A., 2015. A review of artificial intelligence applications in shallow foundations. International Journal of Geotechnical Engineering, 9 (1), 49–60.
  • Shahin, M.A., Jaksa, M.B., and Maier, H.R., 2002b. Artificial Neural Network based Settlement Prediction Formula for Shallow Foundations on Granular Soils. Australian Geomechanics: Journal and News of the Australian Geomechanics Society, 37 (4), 45–52.
  • Shahin, M.A., Maier, H.R., and Jaksa, M.B., 2002a. Predicting settlement of shallow foundations using neural networks. Journal of Geotechnical and Geoenvironmental Engineering, 128 (9), 785–793.
  • Shahnazari, H., Shahin, M.A., and Tutunchian, M.A., 2014. Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils. International Journal of Civil Engineering, 12 (1), 55–64.
  • Stokoe, K., Kacar, O., and Van Pelt, J., 2013. Predicting Settlements of Shallow Footings on Granular Soil Using Nonlinear Dynamic Soil Properties. Paris: Presses des Ponts, 3467–3470.
  • Sulewska, M.J., 2017. Applying Artificial Neural Networks for analysis of geotechnical problems. Computer Assisted Methods in Engineering and Science, 18 (4), 231–241.
  • Terzaghi, K. and Peck, R., 1948. Soil mechanics in engineering practice. New York: John Wiley & Sons.
  • Zhou, J., et al., 2019. Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques. Bulletin of Engineering Geology and the Environment, 79 (4), 2069–2084.

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