KNN based knowledge-sharing model for severe change order disputes in construction

Changes during a construction project are inevitable but many projects are also plagued by severe construction disputes triggered by such changes. These disputes can become time consuming and costly problems which may require litigation to resolve. The objective of this research is to develop a knowledge-sharing model for information sharing that will effectively aid the interested parties to avoid litigious construction change disputes. This model is developed by first establishing a comprehensive database, followed by K Nearest Neighbor (KNN) pattern classification. The data used for the modeling is collected from a nationwide investigation of U.S.A. court records. The model is designed to provide knowledge-sharing linking to the behaviors of similar construction participants with the goal of facing possible serious disputes caused by changes in construction orders. The benefits of this research are not only the development of knowledge modeling but also to help construction practitioners utilize knowledge sharing to prevent unnecessary expense and loss.

[1]  Yacine Rezgui,et al.  Review of information and the state of the art of knowledge management practices in the construction industry , 2001, The Knowledge Engineering Review.

[2]  Michael J. Earl,et al.  Knowledge Management Strategies: Toward a Taxonomy , 2001, J. Manag. Inf. Syst..

[3]  Miklas Scholz,et al.  A comparative study: Prediction of constructed treatment wetland performance with k-nearest neighbors and neural networks , 2006 .

[4]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[5]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[6]  RezguiYacine.  Review of information and the state of the art of knowledge management practices in the construction industry , 2001 .

[7]  C. Anumba,et al.  Conceptual framework for live capture and reuse of project knowledge , 2003 .

[8]  野中 郁次郎,et al.  The knowledge-creating company , 2008 .

[9]  Scott Fernie,et al.  Knowledge Sharing: Context, Controversy and Confusion , 2003 .

[10]  John Messner.  An Architecture for Knowledge Management in the AEC Industry , 2003 .

[11]  Yiming Yang,et al.  High-performing feature selection for text classification , 2002, CIKM '02.

[12]  Asce.  Avoiding and Resolving Disputes during Construction : Successful Practices and Guidelines , 1991 .

[13]  Weng Tat Chan,et al.  Case-based reasoning approach in bid decision making , 2001 .

[14]  Alain Zarli,et al.  REQUIREMENTS AND TRENDS IN ADVANCED TECHNOLOGIES FOR THE LARGE SCALE ENGINEERING UPTAKE , 1998 .

[15]  Patrick S. W. Fong,et al.  Building a Knowledge‐Sharing Culture in Construction Project Teams , 2008 .

[16]  A. Zarli,et al.  Requirements, barriers and trends in advanced technologies for the large scale engineering uptake , 1998 .

[17]  M. Bauer,et al.  Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method , 2001 .

[18]  Peter E.D. Love,et al.  ANN-Based Mark-Up Estimation System with Self-Explanatory Capacities , 1999 .

[19]  Michael C. Vorster,et al.  Innovations to Manage Disputes: DRB and NEC , 2000 .

[20]  Jieh-Haur Chen,et al.  Early Warning System for Severe Change Orders , 2005 .

[21]  Kuo-Ming Chao,et al.  Knowledge sharing and reuse for engineering design integration , 1998 .

[22]  Songbo Tan,et al.  An effective refinement strategy for KNN text classifier , 2006, Expert Syst. Appl..

[23]  Patrick S. W. Fong,et al.  Exploratory Study of Knowledge Sharing in Contracting Companies: A Sociotechnical Perspective , 2006 .

[24]  Hun Hee Cho,et al.  Application of support vector machines in assessing conceptual cost estimates , 2007 .

[25]  Myrian C. A. Costa,et al.  Towards On An Optimized Parallel KNN-FUZZY Classification Approach , 2003 .

[26]  Dongmo Zhang,et al.  A KNN-Based Learning Method for Biology Species Categorization , 2005, ICNC.

[27]  Jieh-Haur Chen,et al.  Hybrid ANN-CBR model for disputed change orders in construction projects , 2007 .

[28]  D. Signorini,et al.  Neural networks , 1995, The Lancet.