Prediction of schedule performance of Indian construction projects using an artificial neural network

References

• Atkinson , R. 1999 . Project management: cost, time and quality, two best guesses and a phenomenon, it’s time to accept other criteria . International Journal of Project Management , 17 ( 6 ) : 337 – 342 .
   Google Scholar
• CFI (2005) Gearing Construction for Growth, Report, Construction Federation of India, January.
   Google Scholar
• Chan , A.P.C. , Ho , D.C.K. and Tam , C.M. 2001 . Design and build project success: multivariate analysis . ASCE Journal of Construction Engineering and Management , 127 ( 2 ) : 93 – 100 .
   Web of Science ®Google Scholar
• Cheung , S.O. , Wong , P.S.P. , Fung , A.S.Y. and Coffy , W.V. 2005 . Predicting project performance through neural networks . International Journal of Project Management , 24 : 207 – 215 .
   Google Scholar
• Chua , D.K.H. , Loh , P.K. , Kog , Y.C. and Jaselskis , E.J. 1997 . Neural networks for construction project success . Expert Systems with Applications , 13 ( 4 ) : 317 – 328 .
   Web of Science ®Google Scholar
• Chua , D.K.H. , Kog , Y.C. and Loh , P.K. 1999 . Critical success factors for different project objectives . ASCE Journal of Construction Engineering and Management , 125 ( 3 ) : 142 – 150 .
   Web of Science ®Google Scholar
• de Wit , A. 1988 . Measurement of project success . Project Management Journal , 6 ( 3 ) : 164 – 170 .
   Google Scholar
• Dvir , D. , Ben-David , A. , Sadeh , A. and Shenhar , A.J. 2006 . Critical managerial factors affecting defense projects success: a comparison between neural network and regression analysis . Engineering Applications of Artificial Intelligence , 19 : 535 – 543 .
   Web of Science ®Google Scholar
• Frigge , M. , Hoaglin , D.C. and Iglewicz , B. 1989 . Some implementations of the boxplot . American Statistician , 43 ( 1 ) : 50 – 54 .
   Web of Science ®Google Scholar
• Goh , A.T.C. 1995 . Back propagation neural networks for modeling complex systems . Artificial Intelligence in Engineering , 9 : 143 – 151 .
   Google Scholar
• Iyer , K.C. and Jha , K.N. 2005 . Factors affecting cost performance: evidence from Indian construction projects . International Journal of Project Management , 23 ( 4 ) : 283 – 295 .
   Google Scholar
• Iyer , K.C. and Jha , K.N. 2006 . Critical factors affecting schedule performance: evidence from Indian construction projects . ASCE Journal of Construction Engineering and Management , 132 ( 8 ) : 871 – 881 .
   Web of Science ®Google Scholar
• Jain , A.K. , Mao , J. and Mohiuddin , K.M. 1996 . Artificial neural networks: a tutorial . IEEE Computer , 29 ( 3 ) : 31 – 44 .
   Web of Science ®Google Scholar
• Jha, K.N. (2004) Factors for the success of a construction project: an empirical study, Doctoral dissertation, Indian Institute of Technology, Delhi.
   Google Scholar
• Jha , K.N. and Iyer , K.C. 2007 . Commitment, coordination, competence and the iron triangle . International Journal of Project Management , 25 : 527 – 540 .
   Google Scholar
• Konchar , M. and Sanvido , A.D. 1998 . Comparison of US project delivery systems . ASCE Journal of Construction Engineering and Management , 124 ( 6 ) : 435 – 444 .
   Web of Science ®Google Scholar
• Krosnick , J.A. 1999 . Survey research . Annual Review of Psychology , 50 : 537 – 567 .
   PubMed Web of Science ®Google Scholar
• Levenberg , K. 1944 . A method for the solution of certain problems in least squares . Quarterly of Applied Mathematics , 2 : 164 – 168 .
   Google Scholar
• Ling , F.Y.Y. and Liu , M. 2004 . Using neural network to predict performance of design-build projects in Singapore . Journal of Building and Environment , 39 : 1263 – 1274 .
   Web of Science ®Google Scholar
• Ling , F.Y.Y. , Chan , S.L. , Chong , E. and Ee , L.P. 2004 . Predicting performance of design-build and design-bid-build projects . ASCE Journal of Construction Engineering and Management , 130 ( 1 ) : 75 – 83 .
   Web of Science ®Google Scholar
• Marquardt , D.W. 1963 . An algorithm for least-squares estimation of nonlinear parameters . Journal of the Society of Industrial and Applied Mathematics , 11 ( 2 ) : 431 – 441 .
   Web of Science ®Google Scholar
• Molenaar , K.R. and Songer , A.D. 1998 . Model for public sector design build project selection . ASCE Journal of Construction Engineering and Management , 124 ( 6 ) : 467 – 479 .
   Web of Science ®Google Scholar
• Soong , T.T. 2004 . Fundamentals of Probability and Statistics for Engineers , Chichester : John Wiley & Sons Ltd .
   Google Scholar
• Zin , M.R. , Mansur , S.A. , Bakri , A. and Caren , T.C.L. Predicting the performance of traditional general contract (TGC) projects: a neural network based approach . Proceedings of the 6th Asia-Pacific Structural Engineering and Construction Conference (APSEC) . 5–6 . September . pp. 78 – 86 . Malaysia : Kuala Lampur .
   Google Scholar
• Zurada , J.M. 1992 . Introduction to Artificial Neural Systems , Los Angeles : West Publishing .
   Google Scholar