Modelo neuro-fuzzy para predição do aporte de energia de diferentes dosagens de concreto em edificações

Abstract

An increasing process of urbanisation and a growing urban population heighten the need to understand the energy costs of the production of building materials. One of the most important tools applied to monitor the use of nonrenewable energy resources in the production of conventional concretes is energy input, into which further research is needed. In this study, an ANFIS (adaptive neuro-fuzzy inference system) hybrid model was developed to predict energy input in order to evaluate the energy demand required for each component of the production of conventional concrete (cement, water, fine aggregate and coarse aggregate) using 101 experimental dosages, 101 validation dosages and energy coefficients available in literature. The results showed that an adequate dosage can generate energy cost savings of 24.77% in the production of concrete, while still maintaining the mechanical characteristics of compressive strength for conventional constructions.