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Assessment of artificial intelligence‐ based techniques for the estimation of pile group scour depth

Autor(en): (Department of Civil Engineering, Faculty of Engineering University of Sistan and Baluchestan Zahedan 9816745845 Iran)
(Department of Civil Engineering and Energy Technology OsloMet—Oslo Metropolitan University Norway)
(Faculty of Architecture and Civil Engineering TU Dortmund University Germany)
(University of Natural Resources and Life Sciences Vienna Austria)
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: ce/papers, , n. 5, v. 6
Seite(n): 1105-1109
DOI: 10.1002/cepa.2037
Abstrakt:

The scour phenomenon around piles is regarded as one of the main causes of serious damages to the pile‐supported structures such as bridges, jetties, wind turbines, and offshore platforms threatening their stability and sustainability in the long term. Thus, accurate forecast of scouring is vital for the design and operation of these structures. In this paper, three artificial intelligence‐based techniques including support vector regression, artificial neural network and random forest were applied to predict the local scour depth around pile groups. An experimental dataset is collected and used to construct the machine learning‐based models. The sediment number, shields parameter spacing, Keulegan‐Carpenter number and pile Reynolds number were used as input variables for the model development. Results assessment indicate that the artificial neural network model anticipated the highest performance among the three machine learning based models, with coefficient of determination of 0.97, and root mean square error of 0.15.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1002/cepa.2037.
  • Über diese
    Datenseite
  • Reference-ID
    10766979
  • Veröffentlicht am:
    17.04.2024
  • Geändert am:
    17.04.2024
 
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