Abstract
A scour depth prediction formula for a river bridge is established using experimental data in which the effects of the pier, pile-cap and pile group are considered. More than 170 experimental data entries, including different pier structural sizes, flow depths and soil covering depths, are collected and verified by existing formulae, which failed to deliver a promising prediction. A machine learning prediction model was then developed to enhance the accuracy. For application purpose, a sequential quadratic programming optimization was adopted to construct an explicit prediction formula. The MAPE was significantly improved from 102.8 to 28.9. The results indicate that the proposed formula can simultaneously satisfy the requirements of accuracy and simplicity. The proposed formula has the advantages of being conceptually consistent with observed scour behaviors and provides a solid scour depth prediction, which is an important and critical step in the bridge safety evaluation if floods are considered.
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