Advertisement

Comparative Evaluation of Learning Curve Models for Construction Productivity Analysis

  • Panagiota Ralli
  • Antonios Panas
  • John-Paris Pantouvakis
  • Dimitrios KaragiannakidisEmail author
Conference paper
  • 68 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

This paper investigates the role of learning curve models in estimating construction productivity. Learning curve theory is actively implemented for both the scheduling and cost estimation of complex construction projects. The purpose of the research is to assess the suitability of published learning curve models in effectively analyzing the learning phenomenon for substantially complex construction operations. The research investigates five (5) learning curve models, namely the (a) Straight-line or Wright, (b) Stanford “B”, (c) Cubic, (d) Piecewise or Stepwise and (e) Exponential models. The methodology includes the comparative implementation of each one of the aforementioned models for the analysis of a large infrastructure project with the use of unit and cumulative productivity data. A two-stage investigative process for the five models was applied in order to define (a) the best-fit model for historical productivity data of completed construction activities and (b) the best predictor model of future performance. The assessment criterion for the suitability is the deviation of the real construction data from the predictions generated by each model. The research results indicate that the Cubic model dominates in terms of its predictive capability on historical data, while the Stanford “B” model is a better future performance predictor. Future research directions include the extension of the research scope with the inclusion of more learning curve models in conjunction with a populated database of historical field data.

Keywords

Construction productivity Estimation Learning curves Statistical analysis 

References

  1. Ammar MA, Samy M (2015) Learning curve modelling of gas pipeline construction in Egypt. Int J Constr Manag 15(3):229–238Google Scholar
  2. Badiru AB (1992) Computational survey of univariate and multivariate learning curve models. IEEE Trans Eng Manage 39(2):176–188CrossRefGoogle Scholar
  3. Carlson J (1973) Cubic learning curves: precision tool for labor estimating. Manuf Eng Manag 67(11):22–25Google Scholar
  4. Couto JP, Teixeira JC (2005) Using linear model for learning curve effect on highrise floor construction. Constr Manag Econ 23(4):355–364CrossRefGoogle Scholar
  5. Everett JG, Farghal S (1994) Learning curve predictors for construction field operations. J Constr Eng Manag 120(3):603–616CrossRefGoogle Scholar
  6. Everett JG, Farghal S (1997) Data representation for predicting performance with learning curves. J Constr Eng Manag 123(1):46–52CrossRefGoogle Scholar
  7. Farghal S, Everett JG (1997) Learning curves: accuracy in predicting future performance. J Constr Eng Manag 123(1):41–45CrossRefGoogle Scholar
  8. Gottlieb SC, Haugbølle K (2010) The repetition effect in building and construction works. Danish Building Research Institute, KobenhavenGoogle Scholar
  9. Hijazi AM, AbouRizk SM, Halpin DW (1992) Modeling and simulating learning development in construction. J Constr Eng Manag 118(4):685–700CrossRefGoogle Scholar
  10. Hinze J, Olbina S (2009) Empirical analysis of the learning curve principle in prestressed concrete piles. J Constr Eng Manag 135(5):425–431CrossRefGoogle Scholar
  11. Jarkas A (2016) Learning effect on labour productivity of repetitive concrete masonry blockwork: Fact or fable? Int J Prod Perform Manag 65(8):1075–1090CrossRefGoogle Scholar
  12. Jarkas A, Horner M (2011) Revisiting the applicability of learning curve theory to formwork labour productivity. Constr Manag Econ 29(5):483–493CrossRefGoogle Scholar
  13. Lee B, Lee H, Park M, Kim H (2015) Influence factors of learning-curve effect in high-rise building constructions. J Constr Eng Manag 141(8):04015019CrossRefGoogle Scholar
  14. Lutz JD, Halpin DW, Wilson JR (1994) Simulation of learning development in repetitive construction. J Constr Eng Manag 120(4):753–773CrossRefGoogle Scholar
  15. Mályusz L, Pém A (2014) Predicting future performance by learning curves. Procedia-Soc Behav Sci 119:368–376CrossRefGoogle Scholar
  16. Naresh AL, Jahren CT (1999) Learning outcomes from construction simulation modeling. Civ Eng Environ Syst 16(2):129–144CrossRefGoogle Scholar
  17. Panas A, Pantouvakis JP (2010) Evaluating research methodology in construction productivity studies. Built Hum Environ Rev 3(1):63–85Google Scholar
  18. Panas A, Pantouvakis JP (2014) Simulation-based and statistical analysis of the learning effect in floating caisson construction operations. J Constr Eng Manag 140(1):04013033CrossRefGoogle Scholar
  19. Panas A, Pantouvakis JP (2018) On the use of learning curves for the estimation of construction productivity. Int J Constr Manag.  https://doi.org/10.1080/15623599.2017.1326302CrossRefGoogle Scholar
  20. Pantouvakis JP, Panas A (2013) Computer simulation and analysis framework for floating caisson construction operations. Autom Constr 36:196–207CrossRefGoogle Scholar
  21. Pellegrino R, Costantino N (2018) An empirical investigation of the learning effect in concrete operations. Eng Constr Archit Manag 25(3):342–357CrossRefGoogle Scholar
  22. Pellegrino R, Costantino N, Pietroforte R, Sancilio S (2012) Construction of multistorey concrete structures in Italy: patterns of productivity and learning curves. Constr Manag Econ 30(2):103–115CrossRefGoogle Scholar
  23. Shan Y, Goodrum PM, Zhai D, Haas C, Caldas CH (2011) The impact of management practices on mechanical construction productivity. Constr Manag Econ 29(3):305–316CrossRefGoogle Scholar
  24. Srour FJ, Kiomjian D, Srour IM (2016) Learning curves in construction: a critical review and new model. J Constr Eng Manag 142:06015004CrossRefGoogle Scholar
  25. Srour FJ, Kiomjian D, Srour IM (2018) Automating the use of learning curve models in construction task duration estimates. J Constr Eng Manag 144(7):04018055CrossRefGoogle Scholar
  26. Thomas HR (2009) Construction learning curves. Pract Period Struct Des Constr 14(1):14–20CrossRefGoogle Scholar
  27. Thomas HR, Mathews CT, Ward JG (1986) Learning curve models of construction productivity. J Constr Eng Manag 112(2):245–259CrossRefGoogle Scholar
  28. United Nations Committee on Housing Building and Planning (1965) Effect of repetition on building operations and processes on site. United Nations, New YorkGoogle Scholar
  29. Zahran K, Nour M, Hosny O (2016) The effect of learning on line of balance scheduling: obstacles and potentials. Int J Eng Sci Comput 6(4):3831–3841Google Scholar
  30. Zayed T, Sharifi MR, Baciu S, Amer M (2008) Slip-form application to concrete structures. J Constr Eng Manag 134:157–168CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Panagiota Ralli
    • 1
  • Antonios Panas
    • 2
  • John-Paris Pantouvakis
    • 2
  • Dimitrios Karagiannakidis
    • 3
    Email author
  1. 1.Mechanical Engineer M.Sc., Hellenic Open University, Department of Projects Contracts and ProcurementBuilding Infrastructures S.AGalatsiGreece
  2. 2.Centre for Construction InnovationNational Technical University of AthensZografouGreece
  3. 3.Civil Engineer, M.Sc.Aristotle University of ThessalonikiGalatsi, AthensGreece

Personalised recommendations