Abstract
In the UAE, continuous flight auger piles (CFA) are the most commonly used type of foundations. To minimize the risk of failure, of these CFA piles, mandatory expensive field tests need to be performed and the most important one is the Static Pile Load Test (SPLT). This paper proposes using a General Regression Neural Network (GRNN) to predict the pile performance ahead of any test. Thousands of loading points in over one hundred projects from Dubai, Abu Dhabi, and Al Ain cities are used to develop a GRNN capable of predicting SPLT curves with reasonable accuracy. The friction angle, unconfined compressive strength, depth, soil type, groundwater table, pile’s diameter, and pile’s length are the parameters that are input to predict the load–displacement curves of the SPLT. This approach can complement conventional SPLT and provide engineers with sufficient insight into the pile performance ahead of the actual test.
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References
Coduto (2001) Foundation design: principles and practices. Prentice Hall, New Jersey
Han J, Kamber M (2012) Data mining, concepts and techniques, 3rd edn. Morgan Kaufman Publishers, MA, USA
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576
Goh ATC (1995) Empirical design in geotechnics using neural networks. Geotechnique 45:709–714
Lee IM, Lee JH (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18(3):189–200
Abu Kiefa MA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng ASCE 124(12):1177–1185
Goh ATC (1996) Pile driving records reanalyzed using neural networks. J Geotech Geoenviron Eng ASCE 122(6):492–495
Shahin MA, Jaska MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 49–62
Benali A, Nechnech A (2011) Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks. In: International seminar, innovation & valorisation in civil engineering & construction materials, N°: 5O-239, University of sciences and technology, Algiers, Algeria
Maizir H, Kassim K (2013) Neural network application in prediction of axial bearing capacity of driven piles. In: Proceedings of the international multiconference of engineers and computer scientists, Hong Kong
Tarawneh B (2013) Pipe pile setup: database and prediction model using artificial neural network. Soils Found 53(4):607–615
Alzo’ubi AK, Ati M, Ibrahim F (2015) Smart framework for predicting drilled shaft capacity based on data mining techniques and GIS data. In: Manzanal D, Sfriso AO (Eds), From fundamentals to applications in geotechnics, the pan american conference on soil mechanics and geotechnical engineering, 15th PCSMGE/8th SCRM/ 6th IS-BA 2015, 15–18, Nov, pp 1909–1915
Fookes PG, Knill JL (1969) The application of engineering geology in the regional development of northern and central Iran. Eng Geol 3:81–120
British standard (2015). BS 8004, Code of practice for foundations, BSI
MathWorks, MATLAB & SIMULINK (2016) Generalized regression networks. https://www.mathworks.com/help/nnet/ug/generalized-regression-neural-networks.html
Acknowledgements
The authors would like to express their gratitude for the office of scholarship and sponsorship programs at Abu Dhabi University, grant number 19300093.
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Alzo’ubi, A.K., Ibrahim, F. (2019). Predicting the Entire Static Load Test by Using Generalized Regression Neural Network in the United Arab Emirates. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_43
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DOI: https://doi.org/10.1007/978-981-13-7082-3_43
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