Smooth support vector regression (SSVR) modelling of self-compacting concrete properties

Self-compacting concrete (SCC) is a type of concrete that can flow under its own weight without vibration, filling small interstices of formwork, passing through complicated geometrical configurations, be pumped through long distances and resist segregation. SCC is a complex material, which makes mo...

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Main Author: Hadiwidodo, Yoyok Setyo
Format: Thesis
Language:English
Published: 2013
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/37060/1/Smooth%20support%20vector%20regression%20%28SSVR%29%20modelling%20of%20self-compacting%20concrete%20properties.pdf
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spelling my-ump-ir.370602023-02-20T07:31:02Z Smooth support vector regression (SSVR) modelling of self-compacting concrete properties 2013-04 Hadiwidodo, Yoyok Setyo TA Engineering (General). Civil engineering (General) Self-compacting concrete (SCC) is a type of concrete that can flow under its own weight without vibration, filling small interstices of formwork, passing through complicated geometrical configurations, be pumped through long distances and resist segregation. SCC is a complex material, which makes modelling its behaviour a very difficult task. SCC constituent materials and mix proportions which must be properly selected to achieve these flow properties required. The effects of any changes in materials or mix proportions on fresh and hardened concrete performance must be considered in evaluating SCC. It is crucial to use a systematic approach for identifying optimal mixes and investigates the most effective factors on SCC properties under a set of constraints. Due to this reason Taguchi method with the L18 (36) orthogonal array is used in this study to investigate the properties of SCC. Taguchi method is a promising approach for optimizing mix proportions of SCC to meet several fresh concrete properties. Taguchi method can simplify the test procedure required to optimize mix proportion of SCC by reducing the number of trial mixes. This study has shown that it is possible to model SCC which fulfilling its criteria. The application of the Taguchi method gave the optimal mix design proportions for fresh properties and hardened properties as well. This study has also demonstrated the capability of regression analysis and Smooth Support Vector Regression (SSVR) modelling to predict the properties of SCC. The performance of the proposed method is evaluated using a coefficient of determination (R2) and mean square error (MSE). Results have shown this model is accurate in prediction of the properties of SCC because it has maximum R2 and minimum MSE. The performance of the proposed method is also verified by comparing the predicted levels with actual values. It can be concluded that SSVR method can predict properties of self-compacting concrete with higher estimation accuracy 2013-04 Thesis http://umpir.ump.edu.my/id/eprint/37060/ http://umpir.ump.edu.my/id/eprint/37060/1/Smooth%20support%20vector%20regression%20%28SSVR%29%20modelling%20of%20self-compacting%20concrete%20properties.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Civil Engineering & Earth Resources Sabaruddin, Mohamad
institution Universiti Malaysia Pahang Al-Sultan Abdullah
collection UMPSA Institutional Repository
language English
advisor Sabaruddin, Mohamad
topic TA Engineering (General)
Civil engineering (General)
spellingShingle TA Engineering (General)
Civil engineering (General)
Hadiwidodo, Yoyok Setyo
Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
description Self-compacting concrete (SCC) is a type of concrete that can flow under its own weight without vibration, filling small interstices of formwork, passing through complicated geometrical configurations, be pumped through long distances and resist segregation. SCC is a complex material, which makes modelling its behaviour a very difficult task. SCC constituent materials and mix proportions which must be properly selected to achieve these flow properties required. The effects of any changes in materials or mix proportions on fresh and hardened concrete performance must be considered in evaluating SCC. It is crucial to use a systematic approach for identifying optimal mixes and investigates the most effective factors on SCC properties under a set of constraints. Due to this reason Taguchi method with the L18 (36) orthogonal array is used in this study to investigate the properties of SCC. Taguchi method is a promising approach for optimizing mix proportions of SCC to meet several fresh concrete properties. Taguchi method can simplify the test procedure required to optimize mix proportion of SCC by reducing the number of trial mixes. This study has shown that it is possible to model SCC which fulfilling its criteria. The application of the Taguchi method gave the optimal mix design proportions for fresh properties and hardened properties as well. This study has also demonstrated the capability of regression analysis and Smooth Support Vector Regression (SSVR) modelling to predict the properties of SCC. The performance of the proposed method is evaluated using a coefficient of determination (R2) and mean square error (MSE). Results have shown this model is accurate in prediction of the properties of SCC because it has maximum R2 and minimum MSE. The performance of the proposed method is also verified by comparing the predicted levels with actual values. It can be concluded that SSVR method can predict properties of self-compacting concrete with higher estimation accuracy
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Hadiwidodo, Yoyok Setyo
author_facet Hadiwidodo, Yoyok Setyo
author_sort Hadiwidodo, Yoyok Setyo
title Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
title_short Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
title_full Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
title_fullStr Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
title_full_unstemmed Smooth support vector regression (SSVR) modelling of self-compacting concrete properties
title_sort smooth support vector regression (ssvr) modelling of self-compacting concrete properties
granting_institution Universiti Malaysia Pahang
granting_department Faculty of Civil Engineering & Earth Resources
publishDate 2013
url http://umpir.ump.edu.my/id/eprint/37060/1/Smooth%20support%20vector%20regression%20%28SSVR%29%20modelling%20of%20self-compacting%20concrete%20properties.pdf
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