Particle swarm optimization model to predict scour depth around a bridge pier

Abstract

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Zounemat-Kermani M, Beheshti A A, Ataie-Ashtiani B, Sabbagh-Yazdi S R. Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied Soft Computing, 2009, 9: 746–755

    Article  Google Scholar 

  2. 2.

    Azamathulla H M, Ghani A A. ANFIS-based approach for predicting the scour depth at culvert outlets. Journal of pipeline systems engineering and practice, 2010, 2: 35–40

    Article  Google Scholar 

  3. 3.

    Richardson E, Davis S. Evaluating Scour at Bridges: Hydraulic Engineering Circular. FHWA-IP-90-017, HEC-18. 2001

  4. 4.

    Johnson P A. Reliability-based pier scour engineering. Journal of Hydraulic Engineering, 1992, 118: 1344–1358

    Article  Google Scholar 

  5. 5.

    Melville B W, Chiew Y M. Time scale for local scour at bridge piers. Journal of Hydraulic Engineering, 1999, 125: 59–65

    Article  Google Scholar 

  6. 6.

    Bateni S M, Borghei S, Jeng D S. Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 2007, 20: 401–414

    Article  Google Scholar 

  7. 7.

    Azamathulla H M, Ghani A A, Zakaria N A, Guven A. Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 2009, 136: 165–169

    Article  Google Scholar 

  8. 8.

    Pal M, Singh N, Tiwari N. M5 model tree for pier scour prediction using field dataset. KSCE Journal of Civil Engineering, 2012, 16: 1079–1084

    Article  Google Scholar 

  9. 9.

    Liao K W, Lu H J, Wang C Y. A probabilistic evaluation of pierscour potential in the Gaoping River Basin of Taiwan. Journal of Civil Engineering and Management, 2015, 21: 637–653

    Article  Google Scholar 

  10. 10.

    Sharafi H, Ebtehaj I, Bonakdari H, Zaji A H. Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Natural Hazards, 2016, 84: 2145–2162

    Article  Google Scholar 

  11. 11.

    Alizadeh M J, Ahmadyar D, Afghantoloee A. Improvement on the existing equations for predicting longitudinal dispersion coefficient. Water Resources Management, 2017, 31: 1777–1794

    Article  Google Scholar 

  12. 12.

    Mottahedi A, Sereshki F, Ataei M. Overbreak prediction in underground excavations using hybrid ANFIS-PSO model. Tunnelling and Underground Space Technology, 2018, 80: 1–9

    Article  Google Scholar 

  13. 13.

    Sreedhara B, Mandal S. Soft Computing for Problem Solving. New York: Springer, 2019, 455–463

    Google Scholar 

  14. 14.

    Al-Musawi A A. Determination of shear strength of steel fiber RC beams: Application of data-intelligence models. Frontiers of Structural and Civil Engineering, 2019, 13(3): 667–673

    Article  Google Scholar 

  15. 15.

    Wang Z X, Li Q. Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model. Journal of Cleaner Production, 2019, 207: 214–224

    Article  Google Scholar 

  16. 16.

    Ghodsi H, Beheshti A A. Evaluation of harmony search optimization to predict local scour depth around complex bridge piers. Civil Engineering Journal, 2018, 4: 402–412

    Article  Google Scholar 

  17. 17.

    Basser H, Karami H, Shamshirband S, Akib S, Amirmojahedi M, Ahmad R, Jahangirzadeh A, Javidnia H. Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike. Applied Soft Computing, 2015, 30: 642–649

    Article  Google Scholar 

  18. 18.

    Fallah S, Deo R, Shojafar M, Conti M, Shamshirband S. Computational intelligence approaches for energy load forecasting in smart energy management grids: State of the art, future challenges, and research directions. Energies, 2018, 11: 596

    Article  Google Scholar 

  19. 19.

    Laucelli D, Giustolisi O. Scour depth modelling by a multi-objective evolutionary paradigm. Environmental Modelling & Software, 2011, 26: 498–509

    Article  Google Scholar 

  20. 20.

    Najafzadeh M, Shiri J, Rezaie-Balf M. New expression-based models to estimate scour depth at clear water conditions in rectangular channels. Marine Georesources and Geotechnology, 2018, 36: 227–235

    Article  Google Scholar 

  21. 21.

    Tinoco R, Goldstein E, Coco G. A data-driven approach to develop physically sound predictors: Application to depth-averaged velocities on flows through submerged arrays of rigid cylinders. Water Resources Research, 2015, 51: 1247–1263

    Article  Google Scholar 

  22. 22.

    Mohamed T A, Pillai S, Noor M J M M, Ghazali A H, Huat B, Yusuf B. Validation of some bridge pier scour formulae and models using field data. Journal of King Saud University-Engineering Sciences, 2006, 19: 31–40

    Article  Google Scholar 

  23. 23.

    Johnson P, Clopper P, Zevenbergen L, Lagasse P. Quantifying uncertainty and reliability in bridge scour estimations. Journal of Hydraulic Engineering, 2015, 141: 04015013

    Article  Google Scholar 

  24. 24.

    Benedict S T, Caldwell A W. A Pier-Scour Database: 2,427 Field and Laboratory Measurements of Pier Scour. Report number: Data Series 84. 2014

  25. 25.

    Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. New York, 1995

  26. 26.

    Laursen E M, Toch A. Scour Around Bridge Piers and Abutments. Ames, IA: Iowa Highway Research Board, 1956

    Google Scholar 

  27. 27.

    El-Saiad A A. Local scour around bridge piers. Engineering Research Journal, 1998, 57: 129–137

    Google Scholar 

  28. 28.

    Riahi-Madvar H, Dehghani M, Seifi A, Salwana E, Shamshirband S, Mosavi A, Chau K W. Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Engineering Applications of Computational Fluid Mechanics, 2019, 13(1): 529–550

    Article  Google Scholar 

  29. 29.

    Melville B, Sutherland A. Design method for local scour at bridge piers. Journal of Hydraulic Engineering, 1988, 114: 1210–1226

    Article  Google Scholar 

  30. 30.

    Mohamed T A, Noor M, Ghazali A H, Huat B B. Validation of some bridge pier scour formulae using field and laboratory data. American Journal of Environmental Sciences, 2005, 1: 119–125

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project and the 2017-1.3.1-VKE-2017-00025 project.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shahaboddin Shamshirband.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shamshirband, S., Mosavi, A. & Rabczuk, T. Particle swarm optimization model to predict scour depth around a bridge pier. Front. Struct. Civ. Eng. (2020). https://doi.org/10.1007/s11709-020-0619-2

Download citation

Keywords

  • scour depth
  • bridge design and construction
  • particle swarm optimization
  • computational mechanics
  • artificial intelligence
  • bridge pier