The productivity and output levels of construction plant and equipment depends in part upon a plant operator's maintenance proficiency; such that a higher degree of proficiency helps ensure that machinery is maintained in good operational order. In the absence of maintenance proficiency, the potential for machine breakdown (and hence lower productivity) is greater. Using data gathered from plant and equipment experts within the UK, plant operators' maintenance proficiency are modelled using a radial basis function (RBF) artificial neural network (ANN). Results indicate that the developed ANN model was able to classify proficiency at 89% accuracy using 10 significant variables. These variables were: working nightshifts, new mechanical innovations, extreme weather conditions, planning skills, operator finger dexterity, years experience with a plant item, working with managers with less knowledge of plant/equipment, operator training by apprenticeship, working under pressure of time and duration of training period. It is proffered that these variables may be used as a basis for categorizing plant operators in terms of maintenance proficiency and, that their potential for influencing operator training programmes needs to be considered.
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PLANT AND EQUIPMENT;
RADIAL BASIS FUNCTION (RBF)
Document Type: Research Article
Off-highway Plant and Equipment Research Centre (OPERC), Department of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire, UK
e-Business Research Centre, School of Management Information Systems, Faculty of Business & Public Management, Edith Cowan University, Joondulap, Australia
Publication date: 2005-12-01