Journal of Engineering, Project, and Production Management, 2020, 10(3), 219-230
Investigation and Comparative Analysis of Learning Curve Models on Construction Productivity: The Case of Caisson Fabrication Process
1Mechanical Engineer, M.Sc.,
Department of Projects Contracts & Procurement, Building Infrastructures
S.A., Moudrou 12 str., 11146 Galatsi, Athens, Greece. E-mail: ralligiota@gmail.com
2Civil Engineer, Ph.D., Centre for Construction Innovation,
National Technical University of Athens, Zografou Campus, Iroon
Polytechniou 9 str., 15780 Zografou, Athens, Greece. E-mail: antpanas@gmail.com
(corresponding author).
3Professor, Centre for Construction Innovation, National
Technical University of Athens, Zografou Campus, Iroon Polytechniou 9
str., 15780 Zografou, Athens, Greece. E-mail: jpp@central.ntua.gr
4Civil Engineer, M.Sc., Aristotle University of Thessaloniki,
Moudrou 30 str., 11146 Galatsi, Athens, Greece. E-mail: di.karagiannakidis@gmail.com
Project Management
Received February 28, 2020; revised April 22, 2020; accepted April 26, 2020
Available online May 24, 2020
Abstract: Learning curves in construction operations analysis is deemed as one of the main factors that determine the variation of on-site productivity and is always taken into account during the planning and estimation stage. This research attempts the assessment of learning curve models’ suitability for the effective analysis of the learning phenomenon for construction operations that are fairly complicated concerning a floating caisson fabrication process for a large-scale marine project, using productivity data. This paper investigates the role of published learning curve models (i.e. Straight-line or Wright; Stanford "B"; Cubic; Piecewise or Stepwise; Exponential) by comparing their outcomes through the use of both unit and cumulative productivity data. There are two main research objectives: first, the model best fitting historical productivity data of construction activities that have been completed are investigated, while secondly, an attempt is made to determine which model better predicts future performance. The less actual construction data deviate from each model’s yielded results, the better their suitability. In the case of unit data, the cubic model fits better historical data, while in the case of future predictions, the Stanford “B” model provides better results. Respectively, the Cubic model yields better results when using cumulative data on historical data and the Straight-line model predicts in a more reliable fashion future performance Possible extensions could be developed in the area of future performance predictions, by adopting different data representation techniques (e.g. moving/exponential weighted average) or by including other (non-classic) learning curve models (e.g. DeJong, Knecht, hyperbolic models).
Keywords: Caisson, construction productivity, learning curve models, learning curves, marine projects, repetitive activities, statistical analysis.
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Citation: Ralli, P., Panas, A., Pantouvakis, J. P., and Karagiannakidis, D. (2020). Investigation and Comparative Analysis of Learning Curve Models on Construction Productivity: The Case of Caisson Fabrication Process. Journal of Engineering, Project, and Production Management, 10(3), 219-230.
DOI:
10.2478/jeppm-2020-0024
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