Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine
作者: G. Jayaprakash M. P. Muthuraj
刊名: CMC: Computers, Materials & Continua, 2018, Vol.54 (1), pp.083-102
来源数据库: Tech Science Press
DOI: 10.3970/cmc.2018.054.083
关键词: Relevance Vector MachineSelf-compacting concreteCompressive strengthVariance.
原始语种摘要: 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...
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  • Vector 矢量
  • compacting 热压预缩
  • concrete 具体
  • cement 胶结物
  • machine 机器
  • compressive 压缩的
  • agreement 协议
  • strength 强度
  • probabilistic 概率的
  • binder 粘合剂