Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile
Publication: International Journal of Geomechanics
Volume 18, Issue 6
Abstract
In this article, the results of load-settlement responses in piles bored from cone penetration tests (CPTs) are presented and discussed to present an accurate artificial intelligence (AI) model. Different AI computation methods, including static and dynamic neural networks, namely, feed-forward neural networks (FFNNs) and focused time-delay neural networks (FTDNNs), are presented using an extensive data set of in situ CPTs. Several interpretation diagrams show the performance of the models. The accuracy of the presented models was investigated using the value of root-mean square error (RMSE) and regression (R2) plots. A FFNN model was chosen for CPT result prediction because of its accuracy and simplicity. The results of convergence analysis indicate that the proposed CPT-based design model is promising for predicting load transfer and settlements for axially loaded single bored piles. A simple formula is presented based on neural network parameters. The predicted results were compared with the experimental data, and a good agreement was attained, confirming the reliability of both the FFNN (R2 = 0.9996) and FTDNN (R2 = 0.9995) solutions in this study.
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References
Alkroosh, I., Bahadori, M., Nikraz, H., and Bahadori, A. (2015). “Regressive approach for predicting bearing capacity of bored piles from cone penetration test data.” J. Rock Mech. Geotech. Eng., 7(5), 584–592.
Alkroosh, I., and Nikraz, H. (2011). “Simulating pile load-settlement behavior from CPT data using intelligent computing.” Cent. Eur. J. Eng., 1(3), 295–305.
Alsamman, O. M., and Long, J. H. (1993). “Prediction of drilled shafts axial capacities using CPT results.” Proc., 3rd Int. Conf. on Case Histories in Geotechnical Engineering, Missouri Univ. of Science and Technology Scholars’ Mine, Rolla, MO.
Ardalan, H., Eslami, A., and Nariman-Zadeh, N. (2009). “Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms.” Comput. Geotech., 36(4), 616–625.
Baziar, M. H., Kashkooli, A., and Saeedi-Azizkandi, A. (2012). “Prediction of pile shaft resistance using cone penetration tests (CPTs).” Comput. Geotech, 45, 74–82.
Bhartia, P. K., Gupta, P., Joiner, J., Vasilkov, A., and Bhartia, P. (2016). “Top-of-the-atmosphere shortwave flux estimation from satellite observations: An empirical neural network approach applied with data from the A-train constellation.” Atmos. Meas. Tech., 9(7), 2813–2826.
Chang, P., Yao, Q. F., and Li, G. (2010). Application research on artificial neural network for the precast reinforced concrete piles, Ci-Premier Pte, Singapore.
Erzin, Y., and Ecemis, N. (2015). “The use of neural networks for CPT-based liquefaction screening.” Bull. Eng. Geol. Environ., 74(1), 103–116.
Hung, L. C., and Kim, S. R. (2015). “CPT-based method for toe resistance of driven piles in sand.” Proc. Instit. Civ. Eng. Geotech. Eng., 168(6), 498–513.
Ismail, A., and Jeng, D. S. (2011). “Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model).” Eng. Appl. Artif. Intell., 24(5), 813–821.
Janbu, N. (1976). “Static bearing capacity of friction piles.” Proc., Sechste Europaeische Konferenz Fuer Bodenmechanik Und Grundbau, Institut Fuer Frundbau und Bodenmechanik, TU Wein, Wien, Austria, 479–488.
King, B. A., Bjorneberg, D. L., Trout, T. J., Mateos, L., Araujo, D. F., and Costa, R. N. (2016). “Estimation of furrow irrigation sediment loss using an artificial neural network.” J. Irrig. Drain. Eng., 04015031.
Kordjazi, A., Nejad, F. P., and Jaksa, M. B. (2014). “Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data.” Comput. Geotech., 55, 91–102.
Lee, I. M., and Lee, J. H. (1996). “Prediction of pile bearing capacity using artificial neural networks.” Comput. Geotech., 18(3), 189–200.
Li, M., Wu, H. P., Handroos, H., Ceccarelli, M., and Carbone, G. (2013). “Vibration control for parallel manipulator based on the feed forward control strategy.” Proc., Int. Mechanical Engineering Congress and Exposition, ASME, New York.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Math., 11(2), 431–441.
MATLAB 7.6.0 [Computer software]. MathWorks, Natick, MA.
Momeni, E., Nazir, R., Armaghani, D. J., and Maizir, H. (2014). “Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN.” Meas., 57, 122–131.
Momeni, E., Nazir, R., Armaghani, D. J., and Maizir, H. (2015). “Application of artificial neural network for predicting shaft and tip resistances of concrete piles.” Earth Sci. Res. J., 19(1), 85–93.
Nazir, R., Moayedi, H., Mosallanezhad, M., and Tourtiz, A. (2015). “Appraisal of reliable skin friction variation in a bored pile.” Proc. Instit. Civ. Eng. Geotech. Eng., 168(1), 75–86.
Park, H. I., and Cho, C. W. (2010). “Neural network model for predicting the resistance of driven piles.” Mar. Georesour. Geotechnol., 28(4), 324–344.
Park, H. I., Seok, J. W., and Hwang, D. J. (2006). “Hybrid neural network and genetic algorithm approach to the prediction of bearing capacity of driven piles.” Numerical methods in geotechnical engineering, Taylor & Francis, Abingdon, U.K.
Pucker, T., Bienen, B., and Henke, S. (2013). “CPT based prediction of foundation penetration in siliceous sand.” Appl. Ocean Res., 41, 9–18.
Samui, P. (2008). “Prediction of friction capacity of driven piles in clay using the support vector machine.” Can. Geotech. J., 45(2), 288–295.
Shahin, M. A. (2010). “Intelligent computing for modeling axial capacity of pile foundations.” Can. Geotech. J., 47(2), 230–243.
Shahin, M. A. (2014a). “Artificial intelligence for modeling load-settlement response of axially loaded bored piles.” Proc., 8th European Conf. on Numerical Methods in Geotechnical Engineering, CRC, Boca Raton, FL.
Shahin, M. A. (2014b). “Load-settlement modeling of axially loaded drilled shafts using CPT-based recurrent neural networks.” Int. J. Geomech., 06014012.
Shahin, M. A. (2014c). “Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks.” Soils Found., 54(3), 515–522.
Shahin, M. A. (2016). “State-of-the-art review of some artificial intelligence applications in pile foundations.” Geosci. Front., 7(1), 33–44.
Suman, S., Das, S. K., and Mohanty, R. (2016). “Prediction of friction capacity of driven piles in clay using artificial intelligence techniques.” Int. J. Geotech. Eng., 10(5), 469–475.
Wang, C. H., and Zhang, M. N. (2013). “A study of the prediction of load-settlement curves of bored piles with an improved grey model.” Adv. Mater. Res., 671–674, 3–9.
Wu, Q. Q., Yue, H. H., Liu, R. Q., Ding, L., and Deng, Z. Q. (2014). “Simulation of multi-closed loop control with feed forward control of micro-vibration isolation platform.” Proc., Int. Mechanical Engineering Congress and Exposition, ASME, New York.
Ziaee, S. A., Sadrossadat, E., Alavi, A. H., and Shadmehri, D. M. (2015). “Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: Application and supplementary studies.” Environ. Earth Sci., 73(7), 3417–3431.
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Published In
International Journal of Geomechanics
Volume 18 • Issue 6 • June 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Dec 30, 2016
Accepted: Oct 30, 2017
Published online: Mar 16, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 16, 2018
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