Human resource allocation holds the key to success in the construction of labor-intensive public infrastructures. However, due to the difficulty in obtaining relevant onsite data, there has been a lack of quantitative numerical analysis and discussion in published literature. In this study, a database for assessing human resource allocation in pavement engineering was established by collecting detailed information from various construction projects. Fourteen influence factors were summarized through literature review and consultation with experts in the field. Thirty two road-smoothing projects were then randomly selected. Using the rough set approach and an artificial neural network model, a model for assessing human resource allocation in pavement engineering was developed. The model validity is verified by an average accuracy of 88.63%. Therefore, this proposed model can be viewed as a useful tool for estimating human resource demand in pavement engineering. It can also effectively alert the authority to avoid a shortage in manpower, preventing the construction project from falling behind schedule or even early termination as a result of inappropriate resource allocation.
|Number of pages||8|
|Journal||International Journal of Pavement Research and Technology|
|State||Published - 2013|
- Project human resources
- Rough sets