Prediction of the Lateral Bearing Capacity of Short Piles in Clayey Soils Using Imperialist Competitive Algorithm-Based Artificial Neural Networks
Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.
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 Shahin, M. A., 2010. Intelligent computing for modeling axial capacity of pile foundations, Canadian Geotechnical Journal. 47(2), pp. 230-243.
 Broms, B. B., 1964. Lateral Resistance of Piles in Cohesive Soils", j. Soil Mech. Found. Div., 90(2), pp. 27-63.
 Hansen, J. B. and N. Christensen, 1961. The Ultimate Resistance of Rigid Piles against Transversal Forces; Model Tests with Transversally Loaded Rigid Piles in Sand: Geoteknisk Institut.
 Matlock, H., 1970. Correlations for design of laterally loaded piles in soft clay, Offshore Technology in Civil Engineering’s Hall of Fame Papers from the Early Years, pp. 77-94.
 Reese, L. C. and R. C. Welch, 1975. Lateral loading of deep foundations in stiff clay, Journal of the Geotechnical Engineering Division, 101(7), pp. 633-649.
 Jeanjean, P. 2009. Re-assessment of p-y curves for soft clays from centrifuge testing and finite element modeling, in Offshore Technology Conference, Offshore Technology Conference.
 Lee, I. M. and Lee, J. H. 1996. Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics. 18(3): pp. 189-200.
 Baranti, M. Golshani, A. A. Yasrebi, S. S. 2015. Determination of bearing capacity of displacement piles in sandy soils using artificial neural networks. Modares Civil Engineering Journal, 14. Pp. 27-36. In Persian.
 Das, S. K. and P. K. Basudhar, 2006. Undrained lateral load capacity of piles in clay using artificial neural network, Computers and Geotechnics, 33(8), pp. 454-459.
 Kordjazi, A., F. Pooya Nejad, and M. Jaksa, 2014. Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data, Computers and Geotechnics, 55, pp. 91-102.
 Samui, P., 2008. Prediction of friction capacity of driven piles in clay using the support vector machine, Canadian Geotechnical Journal, 45(2), pp. 288-295.
 Zhang, M. Y., et al., 2012. Intelligent prediction for side friction of large-diameter and superlong steel pipe pile based on support vector machine, Applied Mechanics and Materials, 170, pp. 747-750.
 Liu, Y. J., et al., 2011. Prediction method of vertical ultimate bearing capacity of single pile based on support vector machine, Advanced Materials Research, 168, pp. 2278-2282.
 Ardalan, H., Eslami, A., Nariman-Zadeh, N, 2009. Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms, Computers and Geotechnics, 36, pp. 616-625.
 E. Momeni, R. Nazir, D. Jahed Armaghani, H. Maizir, 2014. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN, Computers and Geotechnics, 57, pp. 122-131.
 Alikroosh, I. Nikraz, H. 2012. Predicting axial capacity of driven piles in cohesive soils using intelligent computing, Engineering Applications of Artificial Intelligence, 25, pp. 618-627.
 Ismaeil, A. Jeng, D.S. 2013. An optimised product - unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles, Engineering Applications of Artificial Intelligence, 26, pp. 2305-2314.
 Ahangar-Asr, A. Javadi, A. A. Johari, A. Chen, Y. 2014. Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions: An intelligent evolutionary approach. Applied Soft Computing, 24, pp. 822-828.
 Mahin Roosta, R. Farrokh, H. 2010. Prediction of stress- strain behavior in gravelly material based on Artificial Neural Networks. Modares Civil Engineering Journal, 14. pp. 83-95. In Persian.
 Mnhaj, Mohammad Baqir, 2004. Computational Intelligence. (Volume 1), Amir Kabir University of Technology, Tahr-e-Shah Center, Professor of Accountancy Center, In Persian.
 Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A., 2014. Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80: pp. 57–67.
 Simpson P. K., 1990. Artificial neural system—foundation, paradigm, application and implementations. Pergamon Press, New York.
 Kosko, B., 1994. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi
 Atashpaz-Gargari, E. and Lucas, C. 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary Computation, CEC. IEEE Congress on. pp. 4661-4667, (2007).
 Rao, K. M. and V. Suresh Kumar. 1996. Measured and predicted response of laterally loaded piles", in sixth international conference and exhibition on piling and deep foundations, India.
 Fausett, L. V., 1994. Fundamentals neural networks: Architecture, algorithms, and applications, Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
 Hornik K, Stinchcombe M, White, H., 1989. Multilayer feed-forward networks are universal approximators. Neural Netw 2: pp. 359–366.
 Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi, A., 2006. Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43: pp. 224–235.
 Sonmez, H, Gokceoglu, C., 2008. Discussion on the paper by H. Gullu and E. Ercelebi, A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 97: pp. 91–93.
 Hajihassani, Mohsen & Jahed Armaghani, Danial & Marto, Aminaton & Tonnizam, Edy., 2014. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment. 10.1007/s10064-014-0657-x.