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 super- vised 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.

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

Entezami, Alireza;Behkamal, Bahareh;De Michele, Carlo;Mariani, Stefano
2023-01-01

Abstract

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 super- vised 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.
2023
The 10th International Electronic Conference on Sensors and Applications
Remote sensing, Structural displacements, Machine learning, Supervised regression, Long-span bridges
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1261366
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