A Resource-based Grey Prediction Model for Construction Progress Management
Date Issued
2011
Date
2011
Author(s)
Chen, Jui-Chun
Abstract
Progress controlling is significant for project managers in construction industries. Accurate prediction of project progress not only helps managers monitor a project immediately but helps contractors reduce the risk of higher overhead costs. This thesis presented a resource-based approach which combines the second-cumulative grey dynamic prediction model for forecasting project progress during the construction phase.
First, we built the improved GM(1,2) prediction model of second-cumulative approach and forecasted progress individually using the five resource factors of obtained cost (the amount received from the owner), actual cost (the amount expended by the contractor), human, machine and material. The result of six cases showed the five factors affected progress prediction and it would be a forecast basis of the multivariate grey prediction model of GM(1,n). Then, we used the the five resource factors to built the second-cumulative GM(1,6) prediction model to establish the prediction performance of project progress. The mean error percentage of cumulative progress prediction within 10% demonstrated that the proposed approach was able to accurately forecast project progress. Finally, we further extend the forecast range. When extended to the term three, the forcasting still within 80%.
In short, this approach of the resource-based and the second-cumulative approach GM(1,6) dynamic prediction model is effective, simple and stable and it is believed to be a suitable tool for progress prediction in construction.
Subjects
Construction project progress
Resource factors
S-Curve
Grey theory
Second-cumulative model
Type
thesis
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