Improving consumption estimation of electrical materials in residential building construction

Estimating the amount of materials necessary for deploying electrical systems in buildings is a difficult task since these systems are complex and highly correlated with the design of other building systems. In Brazil, the usual method for estimating the consumption of electrical materials relies on constant rates per electrical points, incurring in large estimation errors. Furthermore, these rates can only be used in advanced project phases. This paper proposes a novel method for estimating the consumption of electrical materials, based on artificial neural networks (ANNs), by using information from early project stages. A dataset with project attributes was used to construct the ANN estimation models. In order to evaluate these new models, their estimates were compared to results from linear regression and constant rate models. Results showed that the ANN models have better performance when compared to other methods, supporting that ANNs are well suited for nonlinear and multidimensional problems. This investigation, therefore, supports that the method proposed could also be used by construction companies for estimating the consumption of other materials. (C) 2016 Elsevier B.V. All rights reserved.