| Author: | Yip, Hon Lun |
| Title: | General Regression Neural Network based time series modelling for prediction and analysis of construction equipment maintenance costs |
| Degree: | M.Phil. |
| Year: | 2014 |
| Subject: | Construction equipment -- Costs. Building -- Estimates. Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building and Real Estate |
| Pages: | 97 p. : ill. (some col.) ; 30 cm. |
| Language: | English |
| Abstract: | Construction equipment owners and equipment contractors often face the difficulties of forecasting the behaviour of maintenance cost as breakdown of equipment can come in sudden during its servicing period. This poses an uncertainty to equipment owners that future maintenance costs may have severe discrepancy with the estimated maintenance cost under the routine maintenance schedule. This uncertainty in turn adversely affects the financial management and replacement decision making for construction equipment by the owners. This study, which attempts to provide a better solution to this problem, applies a time series analysis based on General Regression Neural Networks (GRNN) model to address the modelling and prediction of construction equipment maintenance costs. The research covers modelling of both fleet maintenance cost and equipment lifecycle maintenance cost to provide a comprehensive analytical modelling framework for construction equipment maintenance cost problem. The results show that the use of time series approach based on GRNN gives a satisfactory result for maintenance cost modelling and prediction for both fleet maintenance cost and equipment lifecycle maintenance cost with some important implications derived by global sensitivity analysis based on the model. And the use of the GRNN model in optimal replacement model provides a near-optimal timing for equipment replacement. |
| Rights: | All rights reserved |
| Access: | open access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b27473089.pdf | For All Users | 2.34 MB | Adobe PDF | View/Open |
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