Tam, C M; Tong, T K L and Tse, S L (2002) Artificial neural networks model for predicting excavator productivity. Engineering, Construction and Architectural Management, 9(5-6), pp. 446-452. ISSN 0969-9988
Abstract
This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with the multiple regression model developed by Edwards & Holt (2000). A neural network using the architecture of multilayer feedforward (MLFF) is used to model the productivity of excavators. Finally, the modelling methods, predictive behaviours and the advantages of each model are discussed. The results show that the ANN model is suitable for mapping the non-linear relationship between excavation activities and the performance of excavators. It concludes that the ANN model is an ideal alternative for estimating the productivity of excavators.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; excavator; multiple regression; productivity |
| Index terms: | multiple regression model, neural network, estimating, mapping, artificial neural network, multilayer, modelling, feedforward, multiple-regression, excavation, productivity |
| Subjects: | modelling and simulation, spatial and geospatial analysis, specialized materials and systems, construction operations, analytical methods, financial and cost management, artificial intelligence, control systems, management, statistical analysis |
| Topics: | Business Strategy, Engineering Principles, Cost Management, Digital Applications, Research Practice, Site Management, Construction Materials |
| Descriptive scope: | 3 PCA |
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