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Title: A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks
Authors: Jazdani-Chamzini, Abdolreza
Zavadskas, Edmundas Kazimieras
Antuchevičienė, Jurgita
Baušys, Romualdas
Keywords: cost estimation
shovel machine
neural network
multivariate regression
hybrid model
Issue Date: 2017
Publisher: MDPI
Citation: Yazdani-Chamzin, A.; Zavadskas, E.K.; Antucheviciene, J.; Bausys, R. 2017. A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks, MDPI 9(12): 1-14
Series/Report no.: 9;12
Abstract: Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.
ISSN: 2073-8994
Appears in Collections:Moksliniai straipsniai / Research articles

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