A new method of prediction based on smooth support vector regression (SSVR) is introduced to resolve the slump flow modelling of self-compacting concrete (SCC). The slump flow is a function of the content of all concrete ingredients, including cement, silica fume, water, superplasticizer, coarse and fine aggregate. In this paper, the basic ideas underlying SSVR are reviewed, and the potential of the SSVR for multiple regression (modelling) problems is demonstrated by applying the method to model of slump flow from experimental data. The results of experimentation indicate that SSVR has excellent performance on slump flow prediction. Compared with traditional prediction method such as second order regression, SSVR has much more accurate and effective to prediction of slump flow and it is very promising result.

Original languageEnglish
Title of host publicationAdvances in Applied Mechanics and Materials
Number of pages6
Publication statusPublished - 2014
EventInternational Conference on Mechanical Engineering, ICOME 2013 - Mataram, Lombok, Indonesia
Duration: 19 Sept 201321 Sept 2013

Publication series

NameApplied Mechanics and Materials
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482


ConferenceInternational Conference on Mechanical Engineering, ICOME 2013
CityMataram, Lombok


  • Self-compacting concrete
  • Slump flow modelling
  • Smooth support vector regression


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