Data-driven Model for Ternary-Blend Concrete Compressive Strength Prediction Using Machine Learning Approach
Construction & building materials(2021)
摘要
Ternary-blend concrete is a complex composite material, and the nonlinearity in its compressive strength behavior is unquestionable. Entirely many models have been developed to accurately predict the ternary-blend concrete compressive strength, such as ANN, SVM, random forest, decision tree, to mention but a few. This study underscores the better predictive performance and successful application of the least square support vector machine (LSSVM), a machine learning model for predicting the compressive strength of ternary-blend concrete. Coupled simulated annealing (CSA) was applied to the LSSVM model as an optimization algorithm. In addition, the genetic programming (GP) model was used as a benchmark model to compare the performance of the LSSVMCSA model. The predictive performance of the LSSVM-CSA was compared with that of some of the proposed models in well-known studies where the same datasets were used. The model proposed in this study outperformed other studies, yielding an R2 value of 0.954.
更多查看译文
关键词
Ternary concrete,Blast furnace slag,Fly ash,Compressive strength,Least square support vector machine,Coupled simulated annealing,CSA,LSSVM-CSA,Genetic programming, GP
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要