Dynamic Prediction of Project Success Using Artificial Intelligence

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/8675438
• Authors:
  • Ko, Chien-Ho
  • Cheng, Min-Yuan
• Publication Date: 2007-4

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 316-324
• Serial:
  • Journal of Construction Engineering and Management
  • Volume: 133
  • Issue Number: 4
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9364
  • EISSN: 1943-7862
  • Serial URL: http://ascelibrary.org/journal/jcemd4

Subject/Index Terms

• TRT Terms: Artificial intelligence; Construction management; Fuzzy logic; Genetic algorithms; Neural networks; Project management
• Subject Areas: Administration and Management; Construction; Highways; I50: Construction and Supervision of Construction;

Filing Info

• Accession Number: 01051526
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 3 2007 9:37PM