Construction projects frequently overrun and finish over budget; in part due to the lack of control that construction practitioners have over the construction schedule at a process level. Planning methods such as CPM are not of sufficient detail to allow a practitioner to plan a process to maximise its performance. Thus the aim of this research was to develop a practical, computer-based model to enable practitioners to plan projects at a process level and hence improve their projects. This research has focused upon a specific type of process - those that are stochastic and cyclical. Such processes are difficult to predict and hence control, and they are widespread throughout construction projects. Examples are crane operations, formwork erection and scaffold erection. Initially, a focus was placed on the ready-mixed concrete (RMC) supply process. A discrete event simulation (DES) model of this process was developed and validated, based upon real project data. This model could not provide accurate estimates of the process due to the requirement that a user define the probability distributions that represent the process. This is a complex requirement for a practitioner, the issue became known as the complexity problem and a solution was sought using case-based reasoning (CBR). CBR provides solutions to new problems using knowledge from past ones - exactly what a practitioner was doing in the above simulation model. CBR was shown to be capable of solving the complexity problem. A hybrid model, <i>CBRSim, </i>was formed to fulfil the original aim of this thesis. <i>CBRSim </i>works by: CBR selecting probability distributions (based on user input) that are used in a DES model to accurately recreate the process. CBRSim was validated and modelled the process to within +/- 3% accuracy. <i>CBRSim </i>was then applied to another stochastic and cyclical construction process: earthmoving. <i>CBRSim </i>was found to be more accurate in estimating earthmoving productivity than RMC supply, thus providing an indication of a generic modelling capability.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:651699 |
| Date | January 2005 |
| Creators | Graham, D. P. |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/12054 |
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