Mapping rework causes and effects using artificial neural networks

References

• Arbib , M. A. 2003 . The Handbook of Brain Theory and Neural Networks , 2 , Edited by: Arbib , M. A. Cambridge, MA : MIT Press .
   Google Scholar
• Boritz , J. F. and Kennedy , D. B. 1995 . Effectiveness of neural network types for prediction of business failure . Expert Systems with Applications , 9 : 503 – 512 .
   Web of Science ®Google Scholar
• Burati , J. L. , Farrington , J. J. and Ledbetter , W. B. 1992 . Causes of quality deviations in design and construction . ASCE Journal of Construction Engineering and Management , 118 ( 1 ) : 34 – 49 .
   Web of Science ®Google Scholar
• Construction Industry Institute (CII) . 2001a . The Field Rework Index: Early Warning for Field Rework and Cost Growth , Austin, TX : CII . Research Summary No. 153-1
   Google Scholar
• Construction Industry Institute (CII) . 2001b . An Investigation of Field Rework in Industrial Construction , Austin, TX : CII . Research Summary No. 153-11
   Google Scholar
• Garrett , J. H. 1994 . Where and why artificial neural networks are applicable in civil engineering . Journal of Computing in Civil Engineering , 8 ( 2 ) : 129 – 130 .
   Google Scholar
• Hansen , L. K. and Salamon , P. 1990 . Neural network ensembles . IEEE Transactions on Pattern Analysis and Machine Intelligence , 12 : 993 – 1001 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural Networks: A Comprehensive Foundation , 2 , Upper Saddle River, NJ : Prentice-Hall .
   Google Scholar
• Josephson , P. E. , Larsson , B. and Li , H. 2002 . Illustrative benchmarking rework and rework costs . Journal of Management in Engineering , 18 ( 2 ) : 76 – 83 .
   Web of Science ®Google Scholar
• Kalogirou , S. A. and Bojic , M. 2000 . Artificial neural networks for the prediction of the energy consumption of a passive solar building . Energy , 25 : 479 – 491 .
   Web of Science ®Google Scholar
• Kumar , U. A. 2005 . Comparison of neural networks and regression analysis: a new insight . Expert Systems with Applications , 29 : 424 – 430 .
   Web of Science ®Google Scholar
• Love , P. E.D. 2002 . Influence of project type and procurement method on rework costs in building construction projects . Journal of Construction Engineering and Management , 128 ( 1 ) : 18 – 29 .
   Web of Science ®Google Scholar
• Love , P. E.D. and Li , H. 2000 . Quantifying the causes and costs of rework in construction . Construction Management and Economics , 18 ( 4 ) : 479 – 490 .
   Google Scholar
• Love , P. E.D. , Edwards , D. J. and Smith , J. 2005 . Forensic analysis of the causal mechanisms of rework in a structural steel supply chain . Managerial Auditing Journal , 20 ( 2 ) : 187 – 197 .
   Google Scholar
• Love , P. E.D. , Irani , Z. and Edwards , D. J. 2004 . A rework reduction model for construction projects . IEEE Transactions of Engineering Management , 51 ( 4 ) : 426 – 440 .
   Web of Science ®Google Scholar
• Nash , J. E. and Sutcliffe , J. V. 1970 . River flow forecasting through conceptual models Part I – A discussion of principles . Journal of Hydrology , 10 ( 3 ) : 282 – 290 .
   Google Scholar
• Nunnally , J. C. and Bernstein , I. H. 1994 . Psychometric Theory , 3 , New York, NY : McGraw-Hill .
   Google Scholar
• Peretto , P. 1992 . An Introduction to Modeling of Neural Networks , Cambridge : Cambridge University Press .
   Google Scholar
• Pham , D. T. and Liu , X. 1995 . Neural Networks for Identification, Prediction and Control , London : Springer .
   Google Scholar
• Rafiq , M. Y. , Bugmann , G. and Easterbrook , D. J. 1999 . “ Guidelines for designing neural networks for civil engineering applications ” . In Artificial Applications in Civil and Structural Engineering , Edited by: Kumar , B. and Topping , B. H.V. 109 – 115 . Edinburgh : Civil-Comp Press .
   Google Scholar
• Rutkowski , L. 2004 . Generalized regression neural networks in time-varying environment . IEEE Transactions on Neural Networks , 15 ( 3 ) : 576 – 596 .
   Google Scholar
• Santos , J. R. 1999 . Cronbach's alpha: a tool for assessing the reliability of scales . Journal of Extension , 37 ( 2 ) (e-journal available at: http://www.joe.org/joe/1999april/tt3.html)
   Google Scholar
• SAS Institute . 2002 . JMP Statistics and Graphics Guide , Cary, NC : SAS Institute Inc . Version 5
   Google Scholar
• Specht , D. F. 1991 . A general regression neural network . IEEE Transactions on Neural Networks , 2 ( 6 ) : 568 – 576 .
   PubMed Web of Science ®Google Scholar
• Tetko , I. , Livingstone , D. J. and Luik , A. I. 1995 . Neural network studies 1. Comparison of overfitting and overtraining . Journal of Chemical Information and Computer Sciences , 35 : 826 – 833 .
   Google Scholar
• Vanston , J. H. and Vanston , L. K. 2005 . Testing the tea leaves: evaluating the validity of forecasts . IEEE Engineering Management Review , 33 ( 1 ) : 77 – 84 .
   Google Scholar
• Wichmann , N. L. and Bartlett , E. B. 1997 . Ranking input variables using general regression neural networks . Intelligent Engineering Systems through Artificial Neural Networks , 7 : 959 – 964 .
   Google Scholar