A comparative study of solo and hybrid data driven models for predicting bridge pier scour depth

References

• Amini, A., S. Hamidi, M. Malek, T. Mohammad, A. Shirzadi, and J. Behmanesh. 2019. Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers. Preprints 2019. doi:10.20944/preprints201906.0164.v1.
   Google Scholar
• Azamathulla, H. M., and A. A. Ghani. 2011. Genetic Programming for Longitudinal Dispersion Coefficients in Streams. Water Resources Management 25 (6): 1537–1544. doi:10.1007/s11269-010-9759-9.
   Web of Science ®Google Scholar
• Azamathulla, H., A. Ghani, N. Zakaria, and A. Guven. 2010. Genetic Programming to Predict Bridge Pier Scour. Journal of Hydraulic Engineering 136 (3): 165–169. doi:10.1061/(ASCE)HY.1943-7900.0000133.
   Web of Science ®Google Scholar
• Baghbadorani, D. A., B. Ataie-Ashtiani, A. Beheshti, M. Hadjzaman, and M. Jamali. 2018. Prediction of Current-Induced Local Scour around Complex Piers: Review, Revisit, and Integration. Coastal Engineering 133: 43–58. doi:10.1016/j.coastaleng.2017.12.006.
   Web of Science ®Google Scholar
• Bateni, S. M., S. M. Borghei, and D. S. Jeng. 2007. Neural Network and Neuro-Fuzzy Assessments for Scour Depth around Bridge Piers. Engineering Applications of Artificial Intelligence 20 (3): 401–414. doi:10.1016/j.engappai.2006.06.012.
   Web of Science ®Google Scholar
• Bonakdari, H., and I. Ebtehaj. 2017. Scour Depth Prediction around Bridge Piers Using Neuro-Fuzzy and Neural Network Approaches. World Academy of Science, Engineering and Technology International Journal of Civil and Environmental Engineering 11 (6): 835–840.
   Google Scholar
• Brandimarte, L., P. Paron, and G. Di Baldassarre. 2012. Bridge Pier Scour: A Review of Processes, Measurements and Estimates. Environmental Engineering and Management Journal 11 (5): 975–989. doi:10.30638/eemj.2012.121.
   Web of Science ®Google Scholar
• Cheng, M. Y., M. T. Cao, and Y. W. Wu. 2014. Predicting Equilibrium Scour Depth at Bridge Piers Using Evolutionary Radial Basis Function Neural Network. Journal of Computing in Civil Engineering 29 (5): 04014070. doi:10.1061/(ASCE)CP.1943-5487.0000380.
   Web of Science ®Google Scholar
• Dang, N. M., D. T. Anh, and T. D. Dang. 2019. ANN Optimized by PSO and Firefly Algorithms for Predicting Scour Depths around Bridge Piers. Engineering with Computers: 1–11. doi:10.1007/s00366-019-00824-y.
   Web of Science ®Google Scholar
• Duan, Q., S. Sorooshian, and V. K. Gupta. 1992. Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. Water Resources Research 28 (4): 1015–1031. doi:10.1029/91WR02985.
   Web of Science ®Google Scholar
• Duan, Q. Y., V. K. Gupta, and S. Sorooshian. 1993. Shuffled Complex Evolution Approach for Effective and Efficient Global Minimization. Journal of Optimization Theory 76 (3): 501–521.
   Web of Science ®Google Scholar
• Esmaeili, M., A. Kanani, and A. Jamali. 2017. Prediction of Scour Depth around Inclined Bridge Piers Using Optimized ANFIS with GA. Journal of Hydrosciences and Environment 1 (2): 34–45.
   Google Scholar
• Ferreira, C. 2001. Gene-Expression Programming. A New Adaptive Algorithm for Solving Problems. Complex Systems 13 (2): 87–129.
   Google Scholar
• Gaudio, R., C. Grimaldi, A. Tafarojnoruz, and F. Calomino. 2010. Comparison of Formulae for the Prediction of Scour Depth at Piers. Proceedings of the 1st IAHR European Division Congress, Edinburgh, UK, 4–6.
   Google Scholar
• Geem, Z. W., J. H. Kim, and G. Loganathan. 2001. A New Heuristic Optimization Algorithm: harmony Search. Simulation 76 (2): 60–68. doi:10.1177/003754970107600201.
   Google Scholar
• Goel, A. 2019. Prediction of Scour Depth around Bridge Piers Using M5 Model Tree. IWRA (India) Journal (Half Yearly Technical Journal of Indian Geographical Committee of IWRA) 8 (1): 29–34.
   Google Scholar
• Ivakhnenko, A. G. 1968. The Group Method of Data Handling, a Rival of the Method of Stochastic Approximation. Soviet Automatic Control 1 (3): 43–55.
   Google Scholar
• Jamei, M., and I. Ahmadianfar. 2019. Prediction of Scour Depth at Piers with Debris Accumulation Effects Using Linear Genetic Programming. Marine Georesources & Geotechnology: 1–12. doi:10.1080/1064119X.2019.1595793.
   Web of Science ®Google Scholar
• Jang, J. S. R. 1993. ANFIS: Adaptive Network Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23: 665–685. doi:10.1109/21.256541.
   Web of Science ®Google Scholar
• Jang, J. S. R., C. T. Sun, and E. Mizutani. 1997. Neuro-Fuzzy and Soft Computing – a Computational Approach to Leaning and Machine Intelligence. MATLAB Curriculum Series. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Kan, G., K. Liang, J. Li, L. Ding, X. He, Y. Hu, and M. Amo-Boateng. 2016. Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core. Advances in Meteorology 2016: 1–10. doi:10.1155/2016/8483728.
   Web of Science ®Google Scholar
• Karkheiran, S., A. Kabiri-Samani, M. Zekri, and H. M. Azamathulla. 2019. Scour at Bridge Piers in Uniform and Armored Beds under Steady and Unsteady Flow Conditions Using ANN-APSO and ANN-GA Algorithms. ISH Journal of Hydraulic Engineering : 1–9. doi:10.1080/09715010.2019.1617796.
   Google Scholar
• Kaya, A. 2010. Artificial Neural Network Study of Observed Pattern of Scour Depth around Bridge Piers. Computers and Geotechnics 37 (3): 413–418. doi:10.1016/j.compgeo.2009.10.003.
   Web of Science ®Google Scholar
• Kecman, V. 2001. Learning and Soft Computing: Support Vector Machines, Neural Networks and Fuzzy Logic Models. Cambridge, MA: The MIT Press.
   Google Scholar
• Landers, M. N., and D. S. Mueller. 1996. Evaluation of Selected Pier-Scour Equations Using Field Data. Transportation Research Record: Journal of the 1523 (1): 186–195. doi:10.1177/0361198196152300123.
   Google Scholar
• Lee, T. L., D. S. Jeng, G. H. Zhang, and J. H. Hong. 2007. Neural Network Modeling for Estimation of Scour Depth around Bridge Piers. Journal of Hydrodynamics 19 (3): 378–386. doi:10.1016/S1001-6058(07)60073-0.
   Web of Science ®Google Scholar
• Masoumi, M., M. J. Khanjani, and K. Qaderi. 2016. Uplift Capacity Prediction of Suction Caisson in Clay Using a Hybrid Intelligence Method (GMDH-HS). Applied Ocean Research 59: 408–416. doi:10.1016/j.apor.2016.07.005.
   Web of Science ®Google Scholar
• Moradi, F., H. Bonakdari, O. Kisi, I. Ebtehaj, J. Shiri, and B. Gharabaghi. 2019. Abutment Scour Depth Modeling Using Neuro-Fuzzy-Embedded Techniques. Marine Georesources & Geotechnology 37 (2): 190–200. doi:10.1080/1064119X.2017.1420113.
   Web of Science ®Google Scholar
• Moreno, M., R. Maia, and L. Couto. 2016. Prediction of Equilibrium Local Scour Depth at Complex Bridge Piers. Journal of Hydraulic Engineering 142 (11): 04016045. doi:10.1061/(ASCE)HY.1943-7900.0001153.
   Web of Science ®Google Scholar
• Mueller, D. S., and C. R. Wagner. 2005. Field observations and evaluations of streambed scour at bridges. Rep. No. FHWA-RD-03-052, Office of Engineering Research and Development, Federal Highway.
   Google Scholar
• Muttil, N., and A. W. Jayawardena. 2008. Shuffled Complex Evolution Model Calibrating Algorithm: enhancing Its Robustness and Efficiency. Hydrological Processes 22 (23): 4628–4638.
   Web of Science ®Google Scholar
• Muzzammil, M., J. Alam, and M. Danish. 2015. The GMDH Model for Prediction of Scour at Bridge Pier in Cohesive Bed. 20th International Conference on Hydraulics, Water Resources and River Engineering, IIT Roorkee, India, 17–19 December, 2015.
   Google Scholar
• Najafzadeh, M., J. Shiri, and M. Rezaie Balf. 2017. New Expressions-Based Models to Estimate Scour Depth at Clear Water Conditions in Rectangular Channels. Marine Georesources & Geotechnology 36 (2): 227–235. doi:10.1080/1064119X.2017.1303009.
   Web of Science ®Google Scholar
• Pal, M., N. K. Singh, and N. K. Tiwari. 2012. M5 Model Tree for Pier Scour Prediction Using Field Dataset. Journal of Civil Engineering 16 (6): 1079–1084.
   Web of Science ®Google Scholar
• Pandey, M., M. Zakwan, P. K. Sharma, and Z. Ahmad. 2020. Multiple Linear Regression and Genetic Algorithm Approaches to Predict Temporal Scour Depth near Circular Pier in Non-Cohesive Sediment. ISH Journal of Hydraulic Engineering 26 (1): 96–103.
   Google Scholar
• Rahimi, E., K. Qaderi, M. Rahimpour, M. M. Ahmadi, and M. R. Madadi. 2020. Scour at Side by Side Pier and Abutment with Debris Accumulation. Marine Georesources & Geotechnology, 1–12. doi:10.1080/1064119x.2020.1716122.
   Web of Science ®Google Scholar
• Samsudin, R., P. Saad, and A. Shabri. 2011. River Flow Time Series Using Least Squares Support Vector Machines. Hydrology and Earth System Sciences 15 (6): 1835–1852. doi:10.5194/hess-15-1835-2011.
   Web of Science ®Google Scholar
• Sharaf, H., I. Ebtehaj, H. Bonakdari, and A. H. Zaji. 2016. Design of a Support Vector Machine with Different Kernel Functions to Predict Scour Depth around Bridge Piers. Natural Hazards 84 (3): 2145–2162. doi:10.1007/s11069-016-2540-5.
   Web of Science ®Google Scholar
• Smola, A. J. 1996. Regression Estimation with Support Vector Learning Machines. Master’s Thesis. Technische Universitt München, Germany.
   Google Scholar
• Sreedhara, B. M., and S. Mandal. 2019. Swarm Intelligence-Based Support Vector Machine (PSO-SVM) Approach in the Prediction of Scour Depth around the Bridge Pier. In Soft Computing for Problem Solving, 455–463. Singapore: Springer.
   Google Scholar
• U.S. Dept. of Transportation (DOT). 1993. Evaluating scour at bridges. Federal Highway Administration, U.S. Dept. of Transportation, McLean, VA.
   Google Scholar
• U.S. Dept. of Transportation (DOT). 2003. Bridge Scour in Non-uniform Sediment Mixtures and in Cohesive Materials: Synthesis Report. Federal Highway Administration, U.S. Dept. of Transportation, McLean, VA.
   Google Scholar
• Vairappan, C., H. Tamura, S. Gao, and Z. Tan. 2009. Batch Type Local Search-Based Adaptive Neuro-Fuzzy Inference System (ANFIS) with Self-Feedbacks for Time- Series Prediction. Neurocomputing 72 (7–9): 1870–1877. doi:10.1016/j.neucom.2008.05.010.
   Web of Science ®Google Scholar
• Vapnik, V. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
   Google Scholar
• Zounemat-Kermani, M., A. A. Beheshti, B. Ataie-Ashtiani, and S. R. Sabbagh-Yazdi. 2009. Estimation of Current-Induced Scour Depth around Pile Groups Using Neural Network and Adaptive Neuro-Fuzzy Inference System. Applied Soft Computing 9 (2): 746–755. doi:10.1016/j.asoc.2008.09.006.
   Web of Science ®Google Scholar