Analysis of labour efficiency supported by the ensembles of neural networks on the example of steel reinforcement works
Authors:
- M. Juszczyk
Abstract
This study presents an artificial intelligence technique based on ensemble of artificial neural networks for the purposes of analysis and prediction of labour productivity. The study focuses on the development of model that combines several artificial neural networks on the basis of real-life data collected on a construction site for steel reinforcement works. The data includes conditions, characteristics, features of steel reinforcement works and related efficiencies of workers assigned to particular tasks recorded on site. The proposed ensemble based model combines five supervised learning models — five different multilayer perceptron networks, which contribution in the prediction is weighted due to the application of generalised averaging approach. Testing results show that the proposed ensemble based model achieves the satisfactory evaluation criteria for coefficient of correlation (0.989), root-mean-squared error (2.548), mean absolute percentage error (4.65%) and maximum absolute percentage error (8.98%).
- Record ID
- CUTa4e345284a2c443c8ef511854e22127b
- Publication categories
- ;
- Author
- Other language title versions
- Analiza wydajności pracy wspomagana zespołem sieci neuronowych na przykładzie robót zbrojarskich
- Journal series
- Archives of Civil Engineering, ISSN 1230-2945, e-ISSN 2300-3103
- Issue year
- 2020
- Vol
- 66
- No
- 1
- Pages
- 97-111
- Other elements of collation
- tab.; wykr.; Bibliografia (na s.) - 108-109; Bibliografia (liczba pozycji) - 22; Oznaczenie streszczenia - Streszcz. ang., pol.; Numeracja w czasopiśmie - Vol. 66, Iss. 1
- Keywords in Polish
- wydajność pracy, zespoły sieci neuronowych, predykcja, roboty zbrojarskie
- Keywords in English
- labour efficiency, ensembles of neural networks, prediction, steel reinforcement works
- DOI
- DOI:10.24425/ace.2020.131777 Opening in a new tab
- URL
- http://journals.pan.pl/ace/133127 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTa4e345284a2c443c8ef511854e22127b/
- URN
urn:pkr-prod:CUTa4e345284a2c443c8ef511854e22127b
* 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.