Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment. (10th May 2019)

• Record Type: Journal Article
• Authors: Ling, Hao; Qian, Chunxiang; Kang, Wence; Liang, Chengyao; Chen, Huaicheng
• Is Part Of: Construction & building materials. Volume 206(2019)
• Page Start: 355
• Page End: 363
• Publication Date: 2019-05-10
• Subjects: SVM -- K-Fold cross validation -- Degradation -- Concrete strength; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2019.02.071
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: SVM combined with K-Fold cross validation was proposed to predict concrete strength. SVM has better prediction accuracy than Artificial Neural Network and Decision Tree. Suitable input parameters of proposed SVM model were determined and complemented. 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.