The Application of CBR for Improving Forecasting Performance of Periodic Expenditures - Focused on Analysis of Expenditure Progress Curves -

  • Yi, June Seong (Department of Architecture, Ewha Womans University)
  • Received : 2006.02.10
  • Published : 2006.06.30


In spite of enormous increase in data generation, its practical usage in the construction sector has not been prevalent enough compared to those of other industries. The author would explore the obstacles against efficient data application in the arena of expenditure forecasting, and suggest a forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in the research, enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. Among 99 projects collected, the cost data from 88 projects were processed to establish a new forecasting model. The remaining 10 projects were utilized for the validation of the model. From the comprehensive study, the choice of the numbers of referring projects was investigated in detail. It is concluded that selecting similar projects at 12~19 % out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.


Expenditure;Forecasting Model;Cost Data;CBR (Case Based Reasoning);Apartment Housing;Computerization


  1. Aamodt, A. and E. Plaza (1994). 'Case-Based Reasoning: Foundational Issues, Methodological Variations and System Approaches.' AI Communications 7: 39-59
  2. Lee, R.W., Barcia, Ricardo Miranda, and Khator, Suresh K. (1995). 'Case-Based Reasoning for Cash Flow Forecasting Using Fuzzy Retrieval.' First International Conference (ICCBR -- 95)
  3. Pattern, W.N. (1982). 'Application of Cost Flow Forecasting Models.' Journal of Construction Division 17128, 108 C02(3): 226-232
  4. Berny, J. and R. Howes (1982). 'Project Management Control Using Real Time Budgeting and Forecasting Models.' Construction Papers 2: 19-40
  5. Singh, S. and P.W. Woon (1984). 'Cash Flow Trends for High-rise Building Projects.' Proceedings of the 4th International Symposium on Organization and Management of Construction, University of Waterloo, Canada
  6. Stottler, R.H. (1992). 'Case-Based Reasoning for Bid Preparation.' AI Expert(March): 44-49
  7. O''Leary, T. and S.N. Tucker (1996). 'Techniques and Tools for Project Cash Flow Prediction in the Australian Construction Industry.' Proceeding of 40th Annual Meeting of AACE, Vancouver, Canada
  8. Kenley, R. and O. Wilson (1986). 'A Construction Project Cash Flow Model - an Ideographic Approach.' Construction Management and Economics 4: 213-232
  9. Oliver, J.C. (1984). 'Modeling Cash Flow Projections Using a Standard Micro Computer Spreadsheet Program.' Construction Management Projects, Loughborough University of Technology
  10. Ashley, D.B. and P. Teicholz (1977). 'Pre-Estimate Cash Flow Analysis.' Journal of Construction Division 10(3): 111-113
  11. Peterman, G.G. (1972). 'Construction Company Financial Forecasting.' Arizona State University
  12. Allsop, P. (1980). 'Cash Flow and Resource Aggregation from Estimators' Data.' Construction Management, Loughborough University of Technology