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Prediction of local scour depth around bridge piers in clay-sand bed using the ANN method | ||
| Iraqi Journal of Civil Engineering | ||
| Volume 18, Issue 1, June 2024, Pages 50-60 PDF (1015.63 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.37650/ijce.2024.180104 | ||
| Authors | ||
| Abubaker Sami DHEYAB* ; Mustafa GÜNAL | ||
| Gaziantep University, 27310 Şehitkamil, Gaziantep, Türkiye | ||
| Abstract | ||
| Local scour is a primary reason for bridge collapse, presenting a complex challenge due to the numerous factors influencing its occurrence. The complexity of local scour increases with clay-sand beds, particularly in predicting scour depth, as empirical equations are inadequate for such calculations. This study aims to predict local scour around cylindrical bridge piers in clay-sand beds using an artificial neural network (ANN) model. The ANN model was developed using 264 observations from various laboratory experiments. Eight variables were included in the ANN model: clay fraction, pier diameter, flow depth, flow velocity, critical sediment velocity, sediment particle size, bed shear strength, and pier Reynolds number. Sensitivity and statistical analyses were conducted to evaluate the impact of each variable and the accuracy of the ANN model in predicting local scour depth in clay-sand beds. The findings indicate that the ANN model predicted local scour with high accuracy, achieving a mean absolute percentage error (MAPE) of 14.6%. All dimensional variables significantly influenced the prediction of local scour depth, particularly clay fraction and bed shear strength, which were identified as the most crucial parameters. Finally, the MAPE values for local scour depth calculated using empirical equations were significantly higher than those for the ANN model, leading to an overestimation of local scour depth by the empirical equations. | ||
| Keywords | ||
| Local scour; ANN; Pier scour; Clay-sand bed; Cohesion-non cohesion sediment | ||
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