摘要
Accurate pier scour predictions are essential to the safe and efficient design of bridge crossings. Current practice uses empirical formulas largely derived from laboratory experiments to predict local scour depth around single-bridge piers. The resulting formulas are hindered by insufficient consideration of scaling effects and hydrodynamic forces. When applied to full-scale designs, these formula deficiencies lead to excessive over prediction of scour depths and increased construction costs. In an effort to improve the predictive capabilities of the HEC-18 scour model, this work uses field-scale data and nonlinear regression to develop a family of equations optimized for various non-cohesive soil conditions. Improving the predictive capabilities of well-accepted equations saves scarce project dollars without sacrificing safety. To help improve acceptance of modified equations, this work strives to maintain the familiar form of the HEC-18 equation. When compared to the HEC-18 local pier scour equation, this process reduced the mean square error of a validation data set while maintaining over prediction.
Accurate pier scour predictions are essential to the safe and efficient design of bridge crossings. Current practice uses empirical formulas largely derived from laboratory experiments to predict local scour depth around single-bridge piers. The resulting formulas are hindered by insufficient consideration of scaling effects and hydrodynamic forces. When applied to full-scale designs, these formula deficiencies lead to excessive over prediction of scour depths and increased construction costs. In an effort to improve the predictive capabilities of the HEC-18 scour model, this work uses field-scale data and nonlinear regression to develop a family of equations optimized for various non-cohesive soil conditions. Improving the predictive capabilities of well-accepted equations saves scarce project dollars without sacrificing safety. To help improve acceptance of modified equations, this work strives to maintain the familiar form of the HEC-18 equation. When compared to the HEC-18 local pier scour equation, this process reduced the mean square error of a validation data set while maintaining over prediction.
