Research into the financial management of construction plant and equipment maintenance is scant, despite the increased utilisation of mechanisation to augment productivity in recent years. This thesis addresses the shortage of meaningful research by developing a methodology for predicting the total average hourly maintenance costs of tracked hydraulic excavators operating in opencast mining. Initial pilot and field studies conducted revealed that maintenance management (in the form of record keeping and attitude to used oil analysis) within the plant hire and general construction industry was generally poor. Hence, the decision was made to focus the research upon plant operated by opencast mining contractors. Here, plant managers were found to utilise an optimum blend of predictive and fixed-time-to maintenance and also maintain a depth of machine history file data. Modelling total maintenance costs using multiple regression (MR) analysis at the five percent level of significance identified four key predictor variables. These were: machine weight; attitude to used oil analysis (regular use or not); type of industry (opencast coal or slate); and type of machine (backacter or front shovel). However, in order to determine the model's robustness an alternative modelling technique, namely artificial neural networks (ANN) was applied using the same variables identified as significant predictor variables in the regression analysis. Performance analysis conducted on the predictive power of both MR and ANN models revealed that overall the ANN model exhibited greater predictive performance. The thesis concludes with direction for future research and moreover, identifies the need for a more fastidious approach to maintenance management.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:268092 |
| Date | January 1999 |
| Creators | Edwards, David John |
| Publisher | University of Wolverhampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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