Prediction of schedule performance of Indian construction projects using an artificial neural network
Approximately 42% of Indian government-funded construction projects are facing time overruns. With a number of challenging projects around the corner, there is a definite need to overcome these delays. In an earlier study conducted by one of the authors, 55 project performance attributes were identified based on expert opinions and literature surveys, which were subsequently reduced to 20 factors (11 success and nine failure factors) using factor analysis. A second-stage questionnaire survey based on these factors was used to identify the significant schedule performance factors. The analysis of the survey responses led us to conclude that factors such as: a project manager’s competence; monitoring and feedback by project participants; commitment of all project participants; owner’s competence; interaction between external project participants; and good coordination between project participants significantly affect schedule performance. The survey also provided the basis for the development of a schedule performance prediction model. For this, an artificial neural network (ANN) method was used to construct the model, and the best was determined to be a 6-3-1 feed-forward neural network based on a back-propagation algorithm with a mean absolute percentage deviation (MAPD) of 11%. This enables project team members to understand the factors they must monitor closely in order to complete the projects on schedule and to predict performance throughout the course of the project.
Volume (Year): 29 (2011)
Issue (Month): 9 (July)
|Contact details of provider:|| Web page: http://www.tandfonline.com/RCME20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RCME20|
When requesting a correction, please mention this item's handle: RePEc:taf:conmgt:v:29:y:2011:i:9:p:901-911. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.