Kernel Regression Estimator for Damage States of Tunnel Lining Concrete

Abstract

This study evaluates the best estimator for the damage state in the spatial domain and predict the damage growth in temporal domain by the Kernel regression method of conditional random fields. Compared to the classical Kriging of lognormal field approach, the Kernel regression method indicate better performance in specific cases of big data sets.