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primary topic: Estimation of tsunami bore forces on a coastal bridge using an Extreme Learning Machine

Estimation of tsunami bore forces on a coastal bridge using an Extreme Learning Machine
type: AcademicArticle
type: Article
abstract: This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m Ã� 1.5 m Ã� 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge. © 2016 by the authors.
date: 2016
volume: 18
issue: 5
is part of: Entropy
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

MDPI AG
type: Organization

Entropy
type: Collection
issn: 10994300

S. Shamshirband
type: Person
given name: S.
family name: Shamshirband

A.M. Hashim
type: Person
given name: A.M.
family name: Hashim

E. Zalnezhad
type: Person
given name: E.
family name: Zalnezhad

I. Mazinani
type: Person
given name: I.
family name: Mazinani

M. Mansourvar
type: Person
given name: M.
family name: Mansourvar

Z.B. Ismail
type: Person
given name: Z.B.
family name: Ismail

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primary topic: Estimation of tsunami bore forces on a coastal bridge using an Extreme Learning Machine