The nature of statistical learning theory
The nature of statistical learning theory
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Neural network and neuro-fuzzy assessments for scour depth around bridge piers
Engineering Applications of Artificial Intelligence
Application of support vector machines in scour prediction on grade-control structures
Engineering Applications of Artificial Intelligence
Advances in Engineering Software
A general regression neural network
IEEE Transactions on Neural Networks
An optimized instance based learning algorithm for estimation of compressive strength of concrete
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Fuzzy SVM learning control system considering time properties of biped walking samples
Engineering Applications of Artificial Intelligence
Artificial intelligence-based estimation of flushing half-cone geometry
Engineering Applications of Artificial Intelligence
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This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.