Cubic Meter Compressive Strength Prediction of Concrete
来源期刊：Journal Of Wuhan University Of Technology Materials Science Edition2016年第3期
论文作者：龚珍 ZHANG Yimin 胡友健 YU Yan YUAN Yanbin LI Hua
文章页码：590 - 593
摘 要：In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.
龚珍1,2，ZHANG Yimin1，胡友健2，YU Yan1，YUAN Yanbin1，LI Hua1
1. College of Resource and Enviroment Engineering,Wuhan University of Technology2. Information Engineering Institute, China University of Geoscience,Wuhan
摘 要：In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.
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