Pullout capacity of small ground anchors: a relevance vector machine approach

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.