Using the combination of genetic algorithm and artificial neural network to estimate scour depth around bridge foundations

Document Type : Original Article

Authors

1 M. Sc graduated in water structures, Department of Water Engineering, Agricultural Sciences and Natural Resources, Khuzestan University of Agricultural Sciences and Natural Resources, Iran

2 Professor, Department of Water Engineering, Agricultural Sciences and Natural Resources, Khuzestan University of Agricultural Sciences and Natural Resources, Iran

3 Assistant Professor, Department of Water Engineering, Agricultural Sciences and Natural Resources, Khuzestan University of Agricultural Sciences and Natural Resources, Iran

Abstract

Scouring is a natural phenomenon that occurs as a result of the erosive action of water flow in alluvial waterways. This phenomenon is considered a serious threat to the stability of structures located in the flow path, such as the foundations of bridges. One of the most important and effective factors in the destruction and failure of bridges is scouring around bridge foundations and supports. Today, with the progress of science and technology, the use of intelligent computer systems for modeling complex and non-linear phenomena has become increasingly important. In this research, using real data, the efficiency of artificial intelligence systems, which include a combination of multilayer perceptron neural network and genetic algorithm, has been investigated. Among the models with different number of hidden neurons, the artificial neural network with three hidden neurons has the least error. Comparing the values ​​of the difference ratio between the proposed neuro-genetic model and the existing common equations shows that the accuracy of the neuro-genetic model has a higher efficiency compared to other equations. The root mean square error in the proposed model was calculated as 0.51, while this value was calculated above 0.89 for the existing experimental equations.

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References

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Advanced Technologies in Water Efficiency
Volume 2, Issue 3
September 2022
Pages 1-14

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History

  • Receive Date: 06 August 2022
  • Revise Date: 25 September 2022
  • Accept Date: 02 October 2022

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