NDLI: Design of a support vector machine with different kernel functions to predict scour depth around bridge piers

Content Provider	Springer Nature Link
Author	Sharafi, Hassan Ebtehaj, Isa Bonakdari, Hossein Zaji, Amir Hossein
Copyright Year	2016
Abstract	Scour depth is a vital subject in bridge pier design. The exact estimation of scour depth can prevent damage caused by bridge failure and facilitate optimal bridge pier design. In this article, the support vector machine (SVM) method is applied to predict scour depth around bridge piers. The SVM technique is developed using six kernel functions, including polynomial, sigmoid, exponential, Gaussian, Laplacian and rational quadratic. Scour depth is modeled as a function of three dimensionless variables, namely geometric characteristics, flow and bed materials. The performance of SVM (in training and testing) is evaluated using dimensionless variables gathered from a wide range of field datasets. The SVM designed using the polynomial kernel function produced the most accurate results compared with the other kernel functions (RMSE = 0.078, MRE = −0.181, MARE = 0.332, MSRE = 0.025). Sensitivity analysis is performed to identify the effect of each dimensionless parameter on predicting scour depth around bridge piers. The testing results of SVM-polynomial are compared with that of the artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS) and nonlinear regression-based methods presented in this study and in the literature. Evidently, SVM-polynomial predicted scour depth with higher accuracy and lower error than when using ANN, ANFIS and nonlinear regression-based equations. Moreover, as an alternative method, a simple program is presented by the SVM-polynomial to calculate scour depth around bridge piers.
Starting Page	2145
Ending Page	2162
Page Count	18
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	84
Issue Number	3
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-08-26
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bridge pier Scour depth Support vector machine (SVM) Sensitivity analysis Traditional equation Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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