Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

被引：1
作者：
Bhairevi Ganesh Aiyer
Dookie Kim
Nithin Karingattikkal
Pijush Samui
P. Ramamohan Rao
机构：
[1] VIT University,School of Mechanical and Building Science
[2] Kunsan National University,Dept. of Civil Engineering
[3] VIT University,Centre for Disaster Mitigation and Management
来源：
KSCE Journal of Civil Engineering | 2014年 / 18卷
关键词：
compressive strength; concrete; least square support vector machine; relevance vector machine; variance;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This article examines the capability of Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM) for determination of compressive strength (fc) of self compacting concrete. The input variables of LSSVM and RVM are Cement (kg/m3)(C), Fly ash (kg/m3)(F), Water/powder (w/p), Superplasticizer dosage (%)(SP) Sand (kg/m3)(S) and Coarse Aggregate (kg/m3)(CA). The output of LSSVM and RVM is fc. The developed LSSVM and RVM give equations for prediction of fc. A comparative study has been done between the developed LSSVM, RVM and ANN models. Experiments have been conducted to verify the developed RVM and LSSVM. The developed RVM gives variance of the predicted fc. The results confirm that the developed RVM is a robust model for prediction of fc of self compacting concrete.
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页码：1753 / 1758
页数：5
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