Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
