Author(s): Jorge Molines; Gloria Argente; Maria P. Herrera; Josep R. Medina
Linked Author(s):
Keywords: Mound breakwater; Overtopping; Cubic block; Cubipod; Roughness factor
Abstract: Overtopping estimation on mound breakwaters depends on climatic and geometrical variables. The roughness factor (γf) is commonly used in overtopping estimators to take into account the armor unit shape, placement, number of layers and permeability. Usually, theγf given in the literature is associated with the armor unit type and number of layers. In this study, theγf is considered a coefficient to be calibrated and dependent as well on the database used for calibration and the overtopping estimator. The CLASH Neural Network (CLNN) and small-scale tests carried out at the Universitat Politècnica de València are used here to obtain the roughness factor which minimizes the prediction error of double-layer cube armors and single-and double-layer Cubipod armors. The CLNN minimizes the prediction error when usingγf[cubes 2Layers randomly-placed]=0. 54; γf[Cubipod 1Layer]=0. 48 andγf[Cubipod 2Layers]=0. 45, all of these slightly different from theγf values given in the literature. Overtopping discharge of double-layer cube armored mound breakwaters is higher than the discharge of single-and double-layer Cubipod armors.
Year: 2015