ROUGHNESS FACTOR IN OVERTOPPING ESTIMATION

Jorge Molines,Josep Ramon Medina

doi:10.9753/icce.v35.structures.7

Abstract

The roughness factor (Î³f) is a key variable to estimate wave overtopping discharge on mound breakwaters. In this study, the Î³f is re-calibrated using a dataset extracted from the CLASH database. Compared to previous roughness factors calibrated using less restrictive data, overtopping estimators with a few explanatory variables showed variations up to 15% in the 50% percentile of Î³f. On the contrary, the CLASH neural network overtopping predictor showed insignificant variations in the roughness factor, since it is less sensitive to the variability in the data used for calibration. The confidence interval width of the CLASH neural network was narrow compared to simple explicit overtopping estimators, given that it is less sensitive to the number of data used for calibration. The Î³f values used to estimate wave overtopping discharge should be carefully calibrated, especially when using simple empirical formulas.

Highlights

Overtopping on mound breakwaters is usually a key design factor affecting breakwater crest elevation, construction cost and environmental impact
A change in the overtopping predictor or the experimental data used for calibration may significantly change the optimum roughness factor
The roughness factor is dependent on the type of armor, number of layers and placement method, and on the overtopping estimator and database used for calibration

Summary

INTRODUCTION

Overtopping on mound breakwaters is usually a key design factor affecting breakwater crest elevation, construction cost and environmental impact. This breakwater typology is common for large breakwaters using concrete armor units. Using the specific dataset described above and the methodology given by Molines and Medina (2015), roughness factors were re-calibrated for Eq [1], Eq [2], Eq.[3] and the CLASH-NN. ROUGHNESS FACTOR RE-CALIBRATION In this study Eq [1], Eq [2], Eq.[3] and the CLASH-NN are used with the data given by Table 2 to re-calibrate the roughness factors of different types of armor using the methodology described by Molines and Medina (2015). Where N= total number of data, i= data index, Qe and Qo are the estimated and target dimensionless mean overtopping discharges using estimator “e” and target data “o”. 0%

Resample 1

Findings

CONCLUSIONS