Data Representation for Predicting Performance with Learning Curves

Everett, John G.; Farghal, Sherif

doi:10.1061/(asce)0733-9364(1997)123:1(46)

Cited by 30 publications

(14 citation statements)

References 5 publications

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“…In terms of the applied analytical tools, most published research in learning curve productivity analysis adopts the statistical approach for the elaboration of field data (Thomas et al, 1986;Everett and Farghal, 1997;Couto and Texeira, 2005;Pellegrino et al, 2012;Ammar and Samy, 2015;Srour et al, 2016), thus the same approach has been implemented in the current research as well.…”

Section: Learning Curve Theorymentioning

confidence: 99%

“…Learning curve theory has been applied for modeling a variety of construction activities, such as (i) reinforced concrete buildings construction (Couto and Teixeira, 2005;Pellegrino et al, 2012), (ii) off-site fabrication of pre-cast concrete piles (Hinze and Olbina, 2009), (iii) rebar steel and formwork installation (Jarkas, 2010;Jarkas and Horner, 2011;Nguyen andNguyen, 2013, Khanh andKim, 2014), (iv) cell-shaped concrete caissons construction (Panas and Pantouvakis, 2014), (v) gas pipelines construction (Ammar and Samy, 2015) and (vi) megaprojects construction (Everett and Farghal, 1997;Naresh and Jahren, 1999). Thomas et al, 1986) Regardless of the specific applications, the greatest value of learning curves for managers and construction engineers is their predictive capability of future productivity, rather than the assessment of historical productivity data (Farghal and Everett, 1997).…”

Section: Learning Curve Theorymentioning

confidence: 99%

“…The researchers, applying learning curve theory, have to represent learning curve data, using one of the following four techniques: i) unit data, ii) cumulative average data, iii) moving average and iv) exponentially weighted average (Everett and Farghal, 1997;Mályusz and Pém, 2013;Ammar and Samy, 2015). A more detailed description follows in the next paragraph.…”

Section: Data Representationmentioning

confidence: 99%

“…building floor) or to finalize a specific production process in relation to the unit number or cycle number. They replicate the exact result of a specific repetitive activity (Everett and Farghal, 1997;Ammar and Samy, 2015). Cumulative average data represents the average time or cost required to produce or complete a given number of units or cycles (Everett and Farghal, 1997).…”

Section: Data Representationmentioning

confidence: 99%

“…They replicate the exact result of a specific repetitive activity (Everett and Farghal, 1997;Ammar and Samy, 2015). Cumulative average data represents the average time or cost required to produce or complete a given number of units or cycles (Everett and Farghal, 1997). It is computed as a fraction having as the numerator the total construction time or cost for a given number of units (or cycles) and as the denominator the completed units (or cycles) (Hinze and Olbina, 2009;Panas and Pantouvakis, 2014).…”

Section: Data Representationmentioning

confidence: 99%

See 4 more Smart Citations

Investigation and Comparative Analysis of Learning Curve Models on Construction Productivity: The Case of Caisson Fabrication Process

Ralli¹,

Panas

Pantouvakis

et al. 2020

EPPM-Journal

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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. Straightline 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).

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