APPLICATION OF MACHINE LEARNING METHODS TO ESTIMATION OF ADHERED SALT FOR CONCRETE BRIDGES

Abstract

Application of the machine learning methods is attempted to estimate the adhered salt on concrete bridges. The teaching data are obtained through a series of numerical simulations consisting of the three-dimensional fluid analysis around bridge and the random walk analysis. As the machine learning approach, the random forest regression and other four algorithms are considered. Based on the estimation accuracy, the performance of each method is evaluated. Moreover, the capability of the random forest regression is examined in detail, in the context of its application to the present purpose.