Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification

Prediction model for optimized self-compacting concrete with fly ash using response surface... This paper elucidates a data predicting model using an intelligent rule-based enhanced multiclass support vector machine and fuzzy rules (IREMSVM-FR) while optimizing the test practices and trials needed for the proportioning of self- compacting concrete (SCC) using response surface methodology (RSM). The SCC requires a wide range of material content, and hence, more numbers of investigations were typically essential to select a suitable mixture to get the required properties of SCC. Taguchi’s methodology with an L18 array and three-level factor was used to reduce the number of the experiment. Four regulating elements, i.e., cement, fly ash, water powder ratio and superplasticizer, were used. Two results such as slump flow in the fresh state and the compressive strength in the hardened state at 28 days were assessed. Optimizations of the results were set by using RSM. The reactions of material parameters examined to optimize the fresh and hardened properties such as slump flow and compressive strength of SCC. The full quadratic equation of a model can be used to assess the influence of constituent materials on the properties of SCC. Moreover, these 28-days observation records are considered as SCC dataset. For predicting the properties of SCC, an existing intelligent classification http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification

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Publisher
Springer London
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3575-1
Publisher site
See Article on Publisher Site

Abstract

This paper elucidates a data predicting model using an intelligent rule-based enhanced multiclass support vector machine and fuzzy rules (IREMSVM-FR) while optimizing the test practices and trials needed for the proportioning of self- compacting concrete (SCC) using response surface methodology (RSM). The SCC requires a wide range of material content, and hence, more numbers of investigations were typically essential to select a suitable mixture to get the required properties of SCC. Taguchi’s methodology with an L18 array and three-level factor was used to reduce the number of the experiment. Four regulating elements, i.e., cement, fly ash, water powder ratio and superplasticizer, were used. Two results such as slump flow in the fresh state and the compressive strength in the hardened state at 28 days were assessed. Optimizations of the results were set by using RSM. The reactions of material parameters examined to optimize the fresh and hardened properties such as slump flow and compressive strength of SCC. The full quadratic equation of a model can be used to assess the influence of constituent materials on the properties of SCC. Moreover, these 28-days observation records are considered as SCC dataset. For predicting the properties of SCC, an existing intelligent classification

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jun 5, 2018

References

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