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Abstract

Local scour is a serious concern for hydraulic engineers. To maintain the reservoir capacity, flushing accumulated sediment is necessary, resulting in a turbulent and scouring jet from the flushing gate. Predicting the maximum scour depth from these water jets is crucial for civil engineers. Despite many proposed equations, one have shown consistent applicability due to the complex nature of the process. Ensuring accurate prediction between hydraulic parameters and the geometry of the scour hole in prototype experiments remains a key issue. To address this issue, this paper examines the use of artificial neural network (ANN) analysis as a computing device for predicting the maximum local scour depth due to horizontal water jets. The neural network is developed using the data collected from previous experiments and an ongoing study. This paper selected four dimensionless parameters as the key variables: the densimetric Froude number (Frd), the relative roughness (d50/hv), the submergence (ht/hv), and the dimensionless apron length or the length of bed protection (La/hv). This paper describes the development of a feed-forward neural network trained by back-propagation for modeling. This study indicates that the ANN is an effective tool for accurately predicting the scour depth.

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