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Open AccessFeature PaperArticle

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

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Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, 14115/344 Tehran, Iran
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Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania
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Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania
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Department of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania
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Author to whom correspondence should be addressed.
Symmetry 2017, 9(12), 298; https://doi.org/10.3390/sym9120298
Received: 27 October 2017 / Revised: 23 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
(This article belongs to the Special Issue Civil Engineering and Symmetry)
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. View Full-Text
Keywords: cost estimation; shovel machine; neural network; multivariate regression; hybrid model cost estimation; shovel machine; neural network; multivariate regression; hybrid model
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Yazdani-Chamzini, A.; Zavadskas, E.K.; Antucheviciene, J.; Bausys, R. A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks. Symmetry 2017, 9, 298.

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