Oguz, KayaBor Türkben, Aslı2023-06-162023-06-1620220883-95141087-6545https://doi.org/10.1080/08839514.2021.2001734https://hdl.handle.net/20.500.14365/1646The physics of local scour around bridge piers is fairly complex because of multiple forces acting on it. Existing empirical formulas cannot cover all scenarios and soft computing methods require ever greater amounts of data to cover all cases with a single formula or a neural network. The approach proposed in this study brings together observations from over 40 studies, grouping similar observations with hierarchical clustering, and using genetic programming with adaptive operators to evolve formulas specific to each cluster to predict the scour depth. The resulting formulas are made available along with a basic web-based user interface that finds the closest cluster for newly presented data and finds the scour depth using the formula for that cluster. All formulas have R-2 scores over 0.8 and have been validated with validation and testing sets to reduce overfitting. When compared to existing empirical formulas, the generated formulas consistently record higher R-2 scores.eninfo:eu-repo/semantics/openAccessClear-Water ScourNeural-NetworksDepthScaleArticle10.1080/08839514.2021.20017342-s2.0-85121681591