References
- Abdellatif, M. E. M. (2013). Modelling the impact of climae change on Urban Drainage System (PhD thesis). Department of Built Environment, Liverpool John Moores University, UK.
- Abdellatif, M., Atherton, W., Alkhaddar, R., & Osman, Y. (2015). Flood risk assessment for urban water system in a changing climate using artificial neural network. Natural Hazards, 79(2), 1059–1077.
- Akdag, C. T., & Özden, G. (2013). Nonlinear behavior of reinforced concrete (RC) and steel fiber added RC (WS-SFRC) model piles in medium dense sand. Construction and Building Materials, 48, 464–472.
- Al-Gburi, M., Jonasson, J., & Nilsson, M. (2018). Prediction of restraint in second cast sections of concrete culverts using artificial neural networks. European Journal of Environmental and Civil Engineering, 22(2), 226–245. doi:10.1080/19648189.2016.1186116
- Alrashydah, E. I., & Abo-Qudais, S. A. (2018). Modeling of creep compliance behavior in asphalt mixes using multiple regression and artificial neural networks. Construction and Building Materials, 159, 635–641.
- Ardakani, A., & Kordnaeij, A. (2017). Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. European Journal of Environmental and Civil Engineering, 1–14. doi:10.1080/19648189.2017.1304269
- Asteris, P. G., Kolovos, K. G., Douvika, M. G., & Roinos, K. (2016). Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 20, 102–122.
- Baziar, M. H., Azizkandi, A. S., & Kashkooli, A. (2015). Prediction of pile settlement based on cone penetration test results: An ANN approach. KSCE Journal of Civil Engineering, 19(1), 98–106.
- Bowles, J. E. (1978). Engineering properties of soils and their measurement (2nd ed., p. 213). New York (NY): McGraw-Hill International. Book Company.
- BSI (BS EN 1377-7). (1990). Methods of test for soils for civil engineering purposes Part 7: Shear strength tests. London, UK: BSI.
- BSI (BS EN 8004). (1986). Code of practice for foundations. London, UK: BSI.
- Chen, Y., & Kulhawy, F. H. (2002). Evaluation of drained axial capacity of drilled shafts. In Proc. Deep Foundations 2002. Geotech. Spec. Publication No. 116, vol. 2, ASCE, 1200–1214, Reston, Virginia, United States.
- Cho, G. C., Dodds, J., & Santamarina, J. C. (2006). Particle shape effects on packing density, stiffness, and strength: natural and crushed sands. Journal of Geotechnical and Geoenvironmental Engineering, 132(5), 591–602.
- Cho, S. E. (2009). Probabilistic stability analyses of slopes using the ANN-based response surface. Journal of Computers and Geotechnics, 36(5), 787–797.
- Das, B. M. (1995). Principles of foundation engineering (3rd ed.). Boston UA: PWS Publishing Co.
- Deo, R. C., & Şahin, M. (2015). Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in Eastern Australia. Atmospheric Research, 153, 512–525.
- Derbal, I., Bourahla, N., Mebarki, A., & Bahar, R. (2017). Neural network-based prediction of ground time history responses. European Journal of Environmental and Civil Engineering, 1–18. doi:10.1080/19648189.2017.1367727.
- Dyskin, A. V., Estrin, Y., Kanel-Belov, A. J., & Pasternak, E. (2001). Toughening by fragmentation—how topology helps. Advanced Engineering Materials, 3, 885–888.
- Erdal, H. I. (2013). Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Engineering Applications of Artificial Intelligence, 26 (7), 1689–1697.
- Fellenius, B. H. (1988). Unified design of piles and pile groups. Transportation Research Record, 1169, 75–81.
- Fleming, W. A. (1992). New method of single pile settlement and analysis. Geotechnique. 42(3), 411–425.
- Gere, J. M., & Timoshenko, S. P. (1997). Mechanics of materials (4th ed., p. 912). USA: PWS Publishing Company.
- Harandizadeh, H., Toufigh, M. M., & Toufigh, V. (2018). Different neural networks and modal tree method for predicting ultimate bearing capacity of piles. IUST, 8, 311–328.
- Ismail, A. (2017). ANN-based empirical modelling of pile behaviour under static compressive loading. Frontiers of Structural and Civil Engineering, 18(5), 1–15. doi:10.1007/s11709-017-0446-2
- Jardine, R. J., & Chow, F. C. (2007). Some recent development in off-shore pile design. 6th International Offshore Site Investigation Geotechnics Conference, London.
- Jebur, A. A., Atherton, W., & Al Khaddar, R. M. (2018a). Feasibility of an evolutionary artificial intelligence (AI) scheme for modelling of load settlement response of concrete piles embedded in cohesionless soil. Ships and Offshore Structures, 13(7), 705–718.
- Jebur, A. A., Atherton, W., Al Khadar, R. M., & Loffill, E. (2017). Piles in sandy soil: A numerical study and experimental validation. Procedia Engineering, 196, 60–67.
- Jebur, A. A., Atherton, W., Khaddar, R. M. A., & Loffill, E. (2018b). Settlement prediction of model piles embedded in sandy soil using the Levenberg–Marquardt (LM) Training Algorithm. Geotechnical and Geological Engineering, 36(5), 2893–2906. doi:10.1007/s10706-018-0511-1,1-14.
- Jeong, D.-I., & Kim, Y.-O. (2005). Rainfall-runoff models using artificial neural networks for ensemble stream flow prediction. Hydrological Processes, 19(19), 3819–3835.
- Józefiak, K., Zbiciak, A., Maślakowski, M., & Piotrowski, T. (2015). Numerical modelling and bearing capacity analysis of Pile Foundation. Procedia Engineering, 111, 356–363.
- Juncai, X., Qingwen, R., & Zhenzhong, S. (2015). Prediction of the strength of concrete radiation shielding based on LS-SVM. Annals of Nuclear Energy, 85, 296–300.
- Lehane, B. M., & Gavin, K. G. (2001). Base resistance of jacked pipe piles in sand. Journal of Geotechnical and Geoenvironmental Engineering, 127(6), 473–480.
- Łodygowski, T., & Sumelka, W. (2006). Limitations in application of finite element method in acoustic numerical simulation. Journal Theoretical and Applied Mechanics, 44(4), 849–865.
- Loria, R. A. F., Orellana, F., Minardi, A., Fürbringer, J., & Laloui, L. (2015). Predicting the axial capacity of piles in sand. Computers and Geotechnics, 69, 485–495.
- Majeed, A. H., Mahmood, K. R., & Jebur, A. A. (2013). Simulation of Hyperbolic Stress-Strain Parameters of Soils Using Artificial Neural Networks. Procedings of the 23rd International Conference on Geotechnical Engineering (pp. 105–115) Feb 21st - 23rd, Hammamet, Tunisia.
- Masters, T. (1993). Practical Neural Network Recipesin C++. San Diego: Academic.
- McVay, M., Townsend, F. C., Bloomquist, D. G., O'Brien, M. O., & Caliendo, J. A. (1989). Numerical analysis of vertically loaded pile groups. In Proceedings of the Foundation Engineering Congress (pp. 675–690). Illinois. USA: North Western University.
- Meyerhof, G. G. (1976). Bearing capacity and settlement of Pile Foundations. ASCE, Journal of Geotechnical Engineering, 102(GT3), 197–228.
- Momeni, E., Nazir, A., Armaghani, D. J., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122–131.
- Murthy, V. N. S. (2002). Principles and practices of soil mechanics and foundation engineering. Florida, USA: Marcel Dekker Inc.
- Najafzadeh, M. (2015). Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean Engineering, 99, 85–94.
- Najafzadeh, M., Balf, M. R., & Rashedi, E. (2016). Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. Journal of Hydroinformatics, 18(5), 867–884. doi:10.2166/hydro.2016.212.
- Najafzadeh, M., Saberi-Movahed, F., & Sarkamaryan, S. (2018). NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Marine Georesources & Geotechnology, 36(5), 589–602.
- Nguyen-Truong, H. T., & Le, H. M. (2015). An implementation of the Levenberg–Marquardt algorithm forsimultaneous-energy-gradient fitting using two-layer feed forward neural networks. Chemical Physics Letters, 629, 40–45.
- Pallant, J. (2005). SPSS Survival Manual. Australia: Allen & Unwin.
- Poulos, H. G., & Davis, E. H. (1980). Pile Foundation analysis and design. New York: John Wiley & Sons.
- Rafiq, M. Y., Bugmann, G., & Easterbrook, D. J. (2001). Neural network design for engineering applications. Computational and Structures, 79(17), 1541–1552.
- Rajesh, R., & Prakash, J. S. (2011). Extreme learning machines—A review and state of-the-art. International Journal of Wisdom Based Computing, 1, 35–49.
- Reddy, K. M., & Ayothiraman, R. (2015). Experimental studies on behavior of single pile under combined uplift and lateral loading. Journal of Geotechnical and Geoenvironmental Engineering, 141, 1–10.
- Reese, L. C., Isenhower, W. M., & Wang, S. T. (2006). Analysis and design of shallow and deep foundations. New Jersey: John Wiley & Sons.
- Remaud, D. (1999). Pieux Sous Charges Latérales: Etude Expérimentale De L’effet De Groupe (PhD thesis). Université de Nantes, French.
- Rezaei, H., Nazir, R., & Momeni, E. (2016). Bearing capacity of thin-walled shallow foundations: An experimental and artificial intelligence-based study. Journal of Zhejiang University-Science A, 17(4), 273–285.
- Schawmb, T. (2009). The Continuous Helical Displacement pile in comparison to conventional piling techniques (Masters Thesis). University of Dundee, UK.
- Shahin, M. A., & Jaksa, M. B. (2005). Neural network prediction of pullout capacity of marquee ground anchors. Journal of Computers and Geotechnics, 32(3), 153–163.
- Shawash, J. (2012). Generalised Correlation Higher Order Neural Networks, Neural Network operation and Levenberg-Marquardt training on Field Programmable Gate Arrays (PhD thesis). Department of Electronic and Electrical Engineering, University College London, UK.
- Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Allyn and Bacon.
- Tarawneh, B. (2017). Predicting standard penetration test N-value from cone penetration test data using artificial neural networks. Geoscience Frontiers, 8(1), 199–204.
- Taylor, R. N. (1995). Geotechnical centrifuge technology (1st ed.). London: Chapman & Hall.
- Tschuchnigg, F., & Schweiger, H. F. (2015). The embedded pile concept – Verification of an efficient tool for modelling complex deep foundations. Computers and Geotechnics, 63, 244–254.
- Ueno, K. (2000). Methods for preparation of sand samples centrifuge. Proceedings of the International Conference Centrifuge 98. Taylor and Francis: Tokyo, Japan, 23–25.
- Vesic, A. S. (1967). Ultimate load and settlement of deep foundations in sand. Proceedings of the Symposium on Bearing Capacity and Settlement of Foundations, PWS Publishing Company, USA.
- Vesic, A. S. (1977). Design of pile foundations. National cooperative highway research program, synthesis of practice No. 42. Transportation Research Board: Washington, DC.
- Walfish, S. (2006). A review of statistical outlier methods. Pharmaceutical Technology, 30, 82–86.
- Wilamowski, B. M., & Yu, H. (2010). Improved computation for Levenberg-Marquardt training. IEEE Transactions on Neural Networks, 21(6), 930–737.
- Wood, D. M. (2004). Geotechnical modelling. United States of America: Spon Press, Taylor and Francis Group.
- Yadav, A. K., Malik, H., & Chandel, S. S. (2014). Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renewable and Sustainable Energy Reviews, 31, 509–519.
- Zhang, L. M., Xu, Y., & Tang, W. H. (2008). Calibration of models for pile settlement analysis using 64 field load tests. Canadian Geotechnical Journal, 45(1), 59–73.