Cubic Meter Compressive Strength Prediction of Concrete  

作　　者：龚珍 ZHANG Yimin 胡友健 YU Yan YUAN Yanbin LI Hua 

机构地区：[1]College of Resource and Enviroment Engineering,Wuhan University of Technology,Wuhan 430070,China [2]Information Engineering Institute,China University of Geoscience,Wuhan 430070,China

出　　处：《Journal of Wuhan University of Technology(Materials Science)》2016年第3期590-593,共4页武汉理工大学学报（材料科学英文版）

基　　金：Funded by Natioanl Natural Science Foundation of Chin a(Nos.2012BAJ11B00,41301588,41471339,41571514);the Center for Materials Research and Analysis,Wuhan University of Technology

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.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.

关 键 词：cubic meter compressive strength prediction PSO-SVM GA-SVM Grid-SVM 

分 类 号：TU528[建筑科学—建筑技术科学]