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Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Local scour around bridge piers impairs the stability of bridges’ structures. Therefore, a delicate estimation of the local scour depth is vital in designing the bridge piers foundations. In this research, MATLAB software was used to train artificial neural network (ANN) models with four hundred laboratory datasets from different laboratory studies, including five parameters: pier diameter, flow depth flow velocity, critical sediment velocity, sediment particle size, and equilibrium local scour depth. The outcomes present that the ANN model with the Levenberg-Marquardt algorithm and 11 nodes in the single hidden layer gives an accurate estimation better than other ANN models trained with different training algorithms based on the regression results and mean squared error values. Besides, the ANN model accurately provides predicted local scour depth and is better than linear and nonlinear regression models. Furthermore, sensitivity analysis shows that removing pier diameter from training parameters diminishes the reliability of prediction.
Rocznik
Strony
87--101
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
  • University of Gaziantep, Faculty of Engineering, Department of Civil Engineering, Gaziantep, Turkey
  • University of Gaziantep, Faculty of Engineering, Department of Civil Engineering, Gaziantep, Turkey
Bibliografia
  • Amini A., Hamidi S., Shirzadi A., Behmanesh J., Akib S. (2020) Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers, International Journal of River Basin Management, 19 (3), 327–333, https://doi.org/10.1080/15715124.2020.1742138.
  • Azmathullah H. M., Deo M. C., Deolalikar P. B. (2005) Neural networks for estimation of scour downstream of a ski-jump bucket, Journal of Hydraulic Engineering, 131 (10), 898–908, https://doi.org/10.1061/(ASCE)0733-9429(2005)131:10(898).
  • Bateni S. M., Borghei S. M., Jeng D. S. (2007) Neural network and neuro-fuzzy assessments for scour depth around bridge piers, Engineering Applications of Artificial Intelligence, 20 (3), 401–414, https://doi.org/10.1016/j.engappai.2006.06.012.
  • Benedict S., Caldwell A. (2014) A pier-scour database—2,427 field and laboratory measurements of pier scour, doi:10.3133/ds845.
  • Breuser H. N. C., Nicollet G., Shen H. W. (1977) Local scour around cylindrical piers, Journal of Hydraulic Research, 15 (3), 211–252.
  • Chabert J., Engeldinger P. (1956) Etude des affouillements autour des piles de ponts, Chatou, France: Laboratoire National d’Hydraulique.
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  • Chee R. K.W. (1982) Live-bed scour at bridge piers, Publication of Auckland University, New Zealand, (290), http://worldcat.org/issn/01110136.
  • Chiew Y. M. (1984) Local Scour at Bridge Piers, Report No. 355, School of Engg., The Univ. of Auckland, New Zealand.
  • Choi S. U., Cheong S. (2006) Prediction of local scour around bridge piers using artificial neural networks 1, JAWRA Journal of the American Water Resources Association, 42 (2), 487–494, https://doi.org/10.1111/j.1752-1688.2006.tb03852.x.
  • Choi S. U., Choi B., Lee S. (2017) Prediction of local scour around bridge piers using the ANFIS method, Neural Computing and Applications, 28 (2), 335-40-344, https://doi.org/10.1007/s00521-015-2062-1.
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  • Ettema R. (1976) Influence of bed gradation on local scour: New Zealand, University of Auckland, School of Engineering (No. 124), Report.
  • Ettema R. (1980) Scour at bridge piers, Report No. 215, School of Engineering, University of Auckland, Auckland, New Zealand.
  • Ettema R., Kirkil G., Muste M. (2006) Similitude of large-scale turbulence in experiments on local scour at cylinders, Journal of Hydraulic Engineering, 132 (1), 33–40, https://doi.org/10.1061/(ASCE)0733-9429(2006)132:1(33).
  • Fletcher D., Goss E. (1993) Forecasting with neural networks: an application using bankruptcy data, Information & Management, 24 (3), 159–167.
  • GrafW. H. (1995) Local scour around piers, Annual Report, Laboratoire de Recherches Hydrauliques, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, B.33.1–B.33.8.
  • Guven A., Azamathulla H. M., Gunal M. (2012) Predicting wave-induced scour around a circular pile, In: Proceedings of the Institution of Civil Engineers-Maritime Engineering, 165 (1), 31–40, Thomas Telford Ltd, https://doi.org/10.1680/maen.2012.165.1.31.
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  • Hancu S. (1971) Sur le calcul des affouillements locaux dams la zone des piles des ponts, Proceedings of the 14th IAHR Congress, Paris, France, vol. 3, International Association for Hydraulic Research, Delft, The Netherlands, 299–313.
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  • Jain S. C., Fischer, E. E. (1979) Scour around circular bridge piers at high Froude numbers (No. FHWA-RD-79-104 Final Rpt.).
  • Jeng D. S., Bateni S. M., Lockett E. (2005) Neural network assessment for scour depth around bridge piers, The University of Sydney.
  • Khassaf S. I., Abdulwhab A. Q. (2016) Modeling of Local Scour Depth Around Bridge Piers Using Artificial Neural Network, Advances in Natural and Applied Sciences, 10 (11), 71–79.
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  • Laursen E. M., Toch A. (1956) Scour Around Bridge Piers and Abutments, Vol. 4, Ames, IA: Iowa Highway Research Board.
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  • Richardson E.., Harrison L. J., Richardson J., Davis S. R. (1991) Evaluating scour at bridges, Federal Highway Administration Hydraulic Engineering Circular No. 18, Publication No. FHWA-IP-90-017, 105 p.
  • Sarshari E., Mullhaupt P. (2015) Application of Artificial Neural Networks in Assessing The Equilibrium Depth of Local Scour Around Bridge Piers, International Conference on Offshore Mechanics and Arctic Engineering, Vol. 56550, p. V007T06A061, American Society of Mechanical Engineers.
  • Shen H. W., Schneider V. R., Karaki S. S. (1969) Local scour around bridge piers, Journal of the Hydraulics Division, 95 (HY6), 1919–1940.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9faf01f4-bf9c-4b2d-a9ec-b83fdbfc8b6b
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