4 Scopus citations

Abstract

A novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed here. Results from the new approach are compared with existing approaches. Two field datasets from the literature are used in this study. Support vector machine (SVM), which is a machine-learning algorithm, is used to increase the pool of field data samples. For a comprehensive understanding of bridge-pier-scour modeling, a model evaluation function is suggested using an orthogonal projection method on a model performance plot. A fast nondominated sorting genetic algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts. The proposed formulation is compared with two selected empirical models [Hydraulic Engineering Circular No. 18 (HEC-18) and Froehlich equation] and a recently developed data-driven model (gene expression programming model). Results show that the proposed model improves the estimation of critical scour depth compared with the other models.

Original languageEnglish (US)
Article number04014088
JournalJournal of Bridge Engineering
Volume20
Issue number6
DOIs
StatePublished - Jun 1 2015

Keywords

  • Bridges
  • Multiobjective optimization
  • Scour
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

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