Modelling construction site cost index based on neural network ensembles
Authors:
- Michał Juszczyk,
- Agnieszka Leśniak
Abstract
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.
- Record ID
- CUT00bf73dc40bb494e8e6517d0c345e6ba
- Publication categories
- ;
- Author
- Journal series
- Symmetry, ISSN , e-ISSN 2073-8994, Monthly
- Issue year
- 2019
- Vol
- 11
- No
- 3
- Pages
- [1-18]
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 16-18; Bibliografia (liczba pozycji) - 50; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 11, Iss. 3, Spec. Iss.
- Substantive notes
- Tyt. numeru spec.: Multi-Criteria Decision-Making Techniques for Improvement Sustainability Engineering Processes
- Keywords in English
- cost decision making, construction site overhead costs, neural network ensembles, ensemble averaging, stacked generalisation, cost estimation, construction cost management
- DOI
- DOI:10.3390/sym11030411 Opening in a new tab
- URL
- https://www.mdpi.com/2073-8994/11/3/411/xml Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 70
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT00bf73dc40bb494e8e6517d0c345e6ba/
- URN
urn:pkr-prod:CUT00bf73dc40bb494e8e6517d0c345e6ba
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.