Método para prognóstico do consumo de materiais em instalações prediais elétricas utilizando sistemas inteligentes

• Autor(a) principal: Milion, Raphael Negri
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/4700
• topic: Construção civil; Materiais - consumo; Edifícios - instalações elétricas; Sistemas inteligentes; Materials consumption; Electrical building installations; Intelligent systems; ENGENHARIAS::ENGENHARIA CIVIL::CONSTRUCAO CIVIL

Given the importance of forecasting costs in early stages of architectural projects, when it is possible to make changes in the product design and therefore obtain changes in the production costs, and also due to the difficulty of electrical-material consumption prognosis, this research proposes models for predicting electrical-material consumption used in buildings electrical installations. It was used artificial neural networks, an inteligent system, and conventional methods, such as linear regression and consumption rates for the prognostic models. The available data were collected from projects feasibility study and draft design. The research method includes the following steps: a) creation of a database with information collected in quantitatives used for estimates, b) data analysis and preprocessing for use in inteligent and conventional systems, c) attribute selection for best feature identification, i.e, for identifying features with high ability to influence the prognosis and d) development of the models and performance analysis, comparing the predicted values with the actual values. The developed models improves the consumption prognosis performance when compared with common prognostic tools. Current tools consists in multiplying quantitatives by a comsumption rate. Also, the novel models allows more cautious decision-making in projects early design phases, allowing greater awareness of costs impacts. It is expected that this metodology could be used for predicting other building materials.