Comparative study on fuzzy inference systems for prediction of concrete compressive strength

Abstract

The aim of this comparative study is to evaluate the effects of different methods, used for ''aggregation'' and ''defuzzification'', on the output. To reach this goal, six fuzzy inference systems (FIS-1, FIS-2, FIS-3, FIS-4, FIS-5 and FIS-6) have been designed (with the same rule bases) with different methods used for aggregation, and overlapping between the fuzzy sets. The idea of this system is based on the UCI dataset. To design the systems, some of the input fields of UCI dataset have been replaced with other important fields that made system more applicable and suitable. All of these designed systems have the same input fields such as: Water/cement ratio, slump, maximum size of aggregate, coarse aggregate, fine aggregate and age (day). The output field of all systems measures the compressive strength of concrete. These three differences in 401 laboratory samples have caused the average error of predicted compressive strength, that is, 6.43% FIS-1, 6.64% FIS-2, 6.48% FIS-3, 5.56% FIS-4, 4.73% FIS-5 and 5.07% FIS-6. The experimental results reveal that the methods of sum and Centroid (used in the FIS-5) show the best results (among other methods) for the aggregation and the defuzzification, respectively. Key words: Aggregation step, defuzzification step, concrete compressive strength (CCS), fuzzy inference system (FIS), W/C ratio, slump, maximum size of aggregate, coarse aggregate, fine aggregate, age.