Neuro-fuzzy model for energy prediction of different concrete dosages in buildings

Régis Marciano de Souza Ricardo Rodrigues Magalhães Alessandro Torres Campos About the authors

Abstract

Anincreasingprocess of urbanisation anda growing urban population heighten the need to understand the energy costsof the production of building materials. One of the most importanttoolsapplied to monitor the use of non-renewableenergyresources in the production of conventionalconcretes is energy input, intowhichfurther 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 resultsshowedthatan adequate dosage can generate energy cost savingsof 24.77% in the production of concrete, while still maintaining the mechanical characteristics of compressive strength for conventional constructions.

Keywords:
Energy consumption; ANFIS system; Building energy use prediction; Embodied energy; Energy efficiency

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