Modelling Construction Site Cost Index Based on Neural Network Ensembles
Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/3/411 |
| _version_ | 1811184309463154688 |
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| author | Michał Juszczyk Agnieszka Leśniak |
| author_facet | Michał Juszczyk Agnieszka Leśniak |
| author_sort | Michał Juszczyk |
| collection | DOAJ |
| description | Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation. |
| first_indexed | 2024-04-11T13:11:10Z |
| format | Article |
| id | doaj.art-10ca36e71ec7435385d3cb5169832b62 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:11:10Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-10ca36e71ec7435385d3cb5169832b622022-12-22T04:22:35ZengMDPI AGSymmetry2073-89942019-03-0111341110.3390/sym11030411sym11030411Modelling Construction Site Cost Index Based on Neural Network EnsemblesMichał Juszczyk0Agnieszka Leśniak1Cracow University of Technology, Faculty of Civil Engineering, Warszawska 24, 31-155 Cracow, PolandCracow University of Technology, Faculty of Civil Engineering, Warszawska 24, 31-155 Cracow, PolandConstruction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.https://www.mdpi.com/2073-8994/11/3/411cost decision makingconstruction site overhead costsneural network ensemblesensemble averagingstacked generalisationcost estimationconstruction cost management |
| spellingShingle | Michał Juszczyk Agnieszka Leśniak Modelling Construction Site Cost Index Based on Neural Network Ensembles Symmetry cost decision making construction site overhead costs neural network ensembles ensemble averaging stacked generalisation cost estimation construction cost management |
| title | Modelling Construction Site Cost Index Based on Neural Network Ensembles |
| title_full | Modelling Construction Site Cost Index Based on Neural Network Ensembles |
| title_fullStr | Modelling Construction Site Cost Index Based on Neural Network Ensembles |
| title_full_unstemmed | Modelling Construction Site Cost Index Based on Neural Network Ensembles |
| title_short | Modelling Construction Site Cost Index Based on Neural Network Ensembles |
| title_sort | modelling construction site cost index based on neural network ensembles |
| topic | cost decision making construction site overhead costs neural network ensembles ensemble averaging stacked generalisation cost estimation construction cost management |
| url | https://www.mdpi.com/2073-8994/11/3/411 |
| work_keys_str_mv | AT michałjuszczyk modellingconstructionsitecostindexbasedonneuralnetworkensembles AT agnieszkalesniak modellingconstructionsitecostindexbasedonneuralnetworkensembles |