On the General Longshore Transport model for resilient beaches

In the present paper, a set of 319 field and laboratory data on longshore transport rate has been gathered, checked, and finally adopted in order to overcome a noticed limitation in the previous version of General Longshore Transport (GLT) procedure for the estimation of Longshore Transport (LT). In...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Coastal engineering (Amsterdam) 2023-03, Vol.180, p.104257, Article 104257
Hauptverfasser: Tomasicchio, Giuseppe R., Francone, Antonio, Salvadori, Gianfausto
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the present paper, a set of 319 field and laboratory data on longshore transport rate has been gathered, checked, and finally adopted in order to overcome a noticed limitation in the previous version of General Longshore Transport (GLT) procedure for the estimation of Longshore Transport (LT). In particular, the extensive number of additional data, characterized by a wide mobility range, has allowed to obtain a single equation able to determine the longshore transport rate at coastal slopes consisting of loose not-cohesive materials, from reshaping dynamically stable berm breakwaters to sandy beaches. The reliability of the GLT procedure has been assessed by an independent verification with 39 field and laboratory data, for both offshore and breaking wave characteristics as an input. In addition, the GLT procedure has been compared with the van Rijn (2014) formula. •Gathered high quality datasets of longshore transport from field and laboratory experiments.•Calculation of longshore transport rate at slopes consisting of loose not-cohesive materials.•GLT procedure for longshore transport at any coastal mound for both offshore and breaking wave characteristics as an input.•Improvement and verification of the General Longshore Transport procedure on an independent dataset.
ISSN:0378-3839
1872-7379
DOI:10.1016/j.coastaleng.2022.104257