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摘要:
This paper discusses the applicability of relevance vector machine(RVM)based regression to predict the compressive strength of various self compacting concrete(SCC)mixes.Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio,water binder ratio and steel fibres.Relevance vector machine(RVM)is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification.The RVM has an identical functional form to the support vector machine,but provides probabilistic classification and regression.RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation.Compressive strength model has been developed by using MATLAB software for training and prediction.About 75%of the data has been used for development of model and 30%of the data is used for validation.The predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature.
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篇名 Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine
来源期刊 计算机、材料和连续体(英文) 学科 医学
关键词 RELEVANCE VECTOR Machine SELF-COMPACTING concrete COMPRESSIVE strength Variance
年,卷(期) 2018,(1) 所属期刊栏目
研究方向 页码范围 83-102
页数 20页 分类号 R73
字数 语种
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节点文献
RELEVANCE
VECTOR
Machine
SELF-COMPACTING
concrete
COMPRESSIVE
strength
Variance
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研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
计算机、材料和连续体(英文)
月刊
1546-2218
江苏省南京市浦口区东大路2号东大科技园A
出版文献量(篇)
346
总下载数(次)
4
总被引数(次)
0
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