Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection

Estimation of scour depth at bridges with complex pier foundations using support vector... This study aims at establishing machine learning models based on the support vector regression (SVR) for estimating local scour around complex piers under steady clear-water condition. A data set consisting of scour depth measurement cases has been collected to construct the prediction models. The data set includes eight influencing factors that consider aspects of pier geometry, flow property, and river bed material. Moreover, to enhance the performance of the SVR model, filter and wrapper feature selection strategies are used. The research finding is that all feature selection approaches can help to improve the prediction accuracy compared with the SVR model that uses all available features. Notably, the feature selection method based on the variable neighborhood search (VNS) algorithm achieves the best performance (MAPE = 21.65%, R = 0.85). Accordingly, the prediction model produced by SVR and VNS can be useful for assisting decision makers in the task of structural health monitoring as well as the design phase of bridges. Keywords Scour depth prediction  Bridge Scour  Complex pier foundations  Support vector regression Feature selection  Variable neighborhood search 1 Introduction bridge failures in the United States are related to scour [4]. More importantly, scour failures have the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Civil Structural Health Monitoring Springer Journals

Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection

Loading next page...
 
/lp/springer_journal/estimation-of-scour-depth-at-bridges-with-complex-pier-foundations-wIdWppnpKW
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Civil Engineering; Measurement Science and Instrumentation; Vibration, Dynamical Systems, Control
ISSN
2190-5452
eISSN
2190-5479
D.O.I.
10.1007/s13349-018-0287-2
Publisher site
See Article on Publisher Site

Abstract

This study aims at establishing machine learning models based on the support vector regression (SVR) for estimating local scour around complex piers under steady clear-water condition. A data set consisting of scour depth measurement cases has been collected to construct the prediction models. The data set includes eight influencing factors that consider aspects of pier geometry, flow property, and river bed material. Moreover, to enhance the performance of the SVR model, filter and wrapper feature selection strategies are used. The research finding is that all feature selection approaches can help to improve the prediction accuracy compared with the SVR model that uses all available features. Notably, the feature selection method based on the variable neighborhood search (VNS) algorithm achieves the best performance (MAPE = 21.65%, R = 0.85). Accordingly, the prediction model produced by SVR and VNS can be useful for assisting decision makers in the task of structural health monitoring as well as the design phase of bridges. Keywords Scour depth prediction  Bridge Scour  Complex pier foundations  Support vector regression Feature selection  Variable neighborhood search 1 Introduction bridge failures in the United States are related to scour [4]. More importantly, scour failures have the

Journal

Journal of Civil Structural Health MonitoringSpringer Journals

Published: Jun 2, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off