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De Place Hansen, E J and Larsen, J N (2011) Employment and winter construction: a comparative analysis of Denmark and western European countries with a similar climate. Construction Management and Economics, 29(09), 875–90.

Espinoza, R D (2011) Contingency estimating using option pricing theory: closing the gap between theory and practice. Construction Management and Economics, 29(09), 913–27.

Griffith, A (2011) Delivering best value in the small works portfolio of public sector organizations when using preferred contractors. Construction Management and Economics, 29(09), 891–900.

Jha, K N and Chockalingam, C T (2011) Prediction of schedule performance of Indian construction projects using an artificial neural network. Construction Management and Economics, 29(09), 901–11.

  • Type: Journal Article
  • Keywords: construction schedule; India; neural networks; predictions; project management
  • ISBN/ISSN:
  • URL: http://www.tandfonline.com/doi/abs/10.1080/01446193.2011.608691
  • Abstract:
    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.

Jiang, H and Liu, C (2011) Forecasting construction demand: a vector error correction model with dummy variables. Construction Management and Economics, 29(09), 969–79.

McCabe, A, Parker, R and Brown, K (2011) Social outcomes in the construction industry: the case of the Western Australian ‘Percent for Art’ policy. Construction Management and Economics, 29(09), 929–41.

Styhre, A (2011) The overworked site manager: gendered ideologies in the construction industry. Construction Management and Economics, 29(09), 943-955.

Wakchaure, S S and Jha, K N (2011) Prioritization of bridges for maintenance planning using data envelopment analysis. Construction Management and Economics, 29(09), 957–68.