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Neuro Fuzzy Inference Systems for Estimating Normal Concrete Mixture Proportions

Abstract:

Concrete is a very popular construction material utilized in the construction industry. There are several methods for determining the quantities of its components, being the concrete mixture design method by American Concrete Institute (ACI) the most common procedure among others. Construction laboratories have conducted innumerable concrete mixture designs applying different procedures through time, acquiring valuable information that is not being exploited completely. This study used experimental historical information of a construction laboratory in order to develop Sugeno type fuzzy inference systems (FISs) to estimate material proportions for fabricating normal concrete. Fuzzy modeling was accomplished by using subtractive clustering and adaptive neuro fuzzy inference systems (ANFIS) techniques. A Sugeno type FIS was developed for estimating the proportion of each component in a concrete mixture; namely, water-cement ratio, fine and coarse aggregates, where concrete compressive strength and aggregate properties including fineness moduli and abrasion resistance were the input variables. The resulting fuzzy models were able to estimate concrete constituents very well since computed coefficients of determination (i.e., R-squares) were greater than 90% when validating the models. All FISs can be used as mixture design tools to compute material proportions based on past experience when fabricating concrete on the jobsite. Also, the proposed framework illustrated in this research for concrete mixture design could be extended for any particular data set regardless of concrete component characteristics.