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
Neural Computing and Applications – Springer Journals
Published: Jun 5, 2018
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