A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks
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 mul...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/9/12/298 |
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| author | Abdolreza Yazdani-Chamzini Edmundas Kazimieras Zavadskas Jurgita Antucheviciene Romualdas Bausys |
| author_facet | Abdolreza Yazdani-Chamzini Edmundas Kazimieras Zavadskas Jurgita Antucheviciene Romualdas Bausys |
| author_sort | Abdolreza Yazdani-Chamzini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:18:44Z |
| format | Article |
| id | doaj.art-dcc09dfaa6184562ab5f475fdea314bc |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-14T01:18:44Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-dcc09dfaa6184562ab5f475fdea314bc2022-12-22T02:20:44ZengMDPI AGSymmetry2073-89942017-12-0191229810.3390/sym9120298sym9120298A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural NetworksAbdolreza Yazdani-Chamzini0Edmundas Kazimieras Zavadskas1Jurgita Antucheviciene2Romualdas Bausys3Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, 14115/344 Tehran, IranDepartment of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, LithuaniaDepartment of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, LithuaniaDepartment of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, LithuaniaCost 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.https://www.mdpi.com/2073-8994/9/12/298cost estimationshovel machineneural networkmultivariate regressionhybrid model |
| spellingShingle | Abdolreza Yazdani-Chamzini Edmundas Kazimieras Zavadskas Jurgita Antucheviciene Romualdas Bausys A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks Symmetry cost estimation shovel machine neural network multivariate regression hybrid model |
| title | A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks |
| title_full | A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks |
| title_fullStr | A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks |
| title_full_unstemmed | A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks |
| title_short | A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks |
| title_sort | model for shovel capital cost estimation using a hybrid model of multivariate regression and neural networks |
| topic | cost estimation shovel machine neural network multivariate regression hybrid model |
| url | https://www.mdpi.com/2073-8994/9/12/298 |
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