Author: Samui, Pijush

Source: Canadian Geotechnical Journal, Volume 45, Number 2, February 2008 , pp. 288-295(8)

Publisher: NRC Research Press

Abstract:

The support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy (ε) insensitive εloss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.

La machine à soutien vectoriel (« SVM ») est une technique d'apprentissage mécanique en émergence dans laquelle la prédiction de l'erreur et la complexité du modèle sont simultanément minimizées. Cet article examine le potentiel de la « SVM » pour prédire la capacité en frottement de pieux foncés dans l'argile. On a adopté la « SVM » qui est basée solidement sur la théorie d'apprentissage statistique et utilise la technique de régression en introduisant une fonction de perte ε-insensible. Les résultats sont comparés avec un modèle de réseau de neurones artificiels (« ANN ») utilisé fréquemment. Globalement, la « SVM » montre une bonne performance et s'avère être meilleure que le modèle « ANN ». Une analyse de sensibilité a été réalisée pour étudier l'importance des intrants. L'étude montre que la « SVM » a le potentiel d'être un outil utile et pratique pour la prédiction de la capacité en frottement de pieux foncés dans l'argile.

Document Type: Research article

Publication date: 2008-02-01

More about this publication?

• Published since 1963, this monthly journal features articles, notes, and discussions related to new developments in geotechnical and geoenvironmental engineering, and applied sciences. The topics of papers written by researchers, theoreticians, and engineers/scientists active in industry include soil and rock mechanics, material properties and fundamental behaviour, site characterization, foundations, excavations, tunnels, dams and embankments, slopes, landslides, geological and rock engineering, ground improvement, hydrogeology and contaminant hydrogeology, geochemistry, waste management, geosynthetics, offshore engineering, ice, frozen ground and northern engineering, risk and reliability applications, and physical and numerical modelling. Papers on actual case records from practice are encouraged and frequently featured.
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