A Comparative Study on Structural Displacement Prediction by Kernelized Regressors Under Limited Training Data

An accurate prediction of the structural response in the presence of limited training data still represents a big challenge if machine learning-based approaches are adopted. This paper investigates and compares two state-of-the-art kernelized supervised regressors to predict the structural response of a long-span bridge retrieved from spaceborne remote sensing technology. The kernelized supervised procedure is either based on a support vector regression, or on a Gaussian process regression. A small set of displacement time histories and corresponding air temperature data are fed into the regressors, to predict the actual structural response. Results demonstrate that the proposed regression techniques are reliable, even when only 30% of the training data are used at the learning stage.