Analysis of Labour Efficiency Supported by the Ensembles of Neural Networks on the Example of Steel Reinforcement Works

• Published in: Archives of civil engineering 2020-01, Vol.66 (1), p.97-111
• Main Author: Juszczyk, Michał
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; Construction sites; ensembles of neural networks; Errors; Labor productivity; labour efficiency; Multilayer perceptrons; Neural networks; prediction; Steel construction; steel reinforcement works
• ISSN: 1230-2945; 2300-3103
• DOI: 10.24425/ace.2020.131777

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%).