Irrigation Sciences and Engineering

Irrigation Sciences and Engineering

Predicting maximum scour depth around bridge abutment using M5 model

Document Type : Research Paper

Authors
Javad Zahiri 1
Seyed Mahmood Kashefipour 2
1 Assistant Professor, Department of Water Engineering, Khuzestan Ramin Agriculture and Natural Resources University, Iran
2 Professor, Water Sciences Engineering Faculty, Shahid Chamran University, Ahwaz, Iran
10.22055/jise.2018.13543
Abstract
Scour around piers and abutments leads to a lot of bridge failures. Scouring around the bridge piers and abutments is one of the most important factors in bridge failure which happens over time and increases during floods, probably causing general damage to the bridge. Accordingly, the accurate estimation of scouring around the bridge piers and abutments can help engineers to take steps to deal with bridge destructions. Recently, data-driven models have been used widely to estimate the scour depth around bridge piers and abutments. Najafzadeh, Barani and Hessami-Kermani (2015) used a set of data-driven models such as the gravitational search algorithm and the particle swarm optimization algorithm to estimate the scour around bridge abutments. Sediment particles size, geometric characteristics of bridge abutment and approach flow conditions were considered as effective parameters on scouring mechanism. The results of this research showed that data-driven models can estimate the scour depth with high accuracy. In the present study, M5 tree model has been used to predict maximum scour depth around bridge abutment. High accuracy, simplicity and understandability are considered the advantages of this algorithm. A huge amount of data from credible references have been used to build and validate the tree model.
Keywords
bridge abutment
M5 model
Pruning
Flow depth-abutment length
Sensitivity analysis
Subjects
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Irrigation Sciences and Engineering
Volume 41, Issue 1
Spring 2018
Pages 1-16

  • Receive Date 06 May 2015
  • Revise Date 28 July 2016
  • Accept Date 11 September 2016
  • Publish Date 21 April 2018