Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment

Abstract

Abstract Support Vector Machine (SVM) model optimized by K-Fold cross validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, Artificial Neural Network (ANN) and Decision Tree (DT) were built to compare the prediction precision with SVM model. The result shows SVM model has the best prediction performance. For the original SVM model, there is a problem that input parameters of SVM model are incomplete. Based on the analysis of an amount of research papers, number of input parameters increases from original 8 to 19. After the complementation of input parameters, the prediction precision of SVM model gets a good promotion. The average relative error declines from 34.8% to 27.6%, and the median relative error is reduced from 24.70% to 20.8%. The number of data with relative errors less than 40% has reached 396, almost 80% of 500 testing data.