Prediction of the learning curve in roof insulation

Mályusz, Levente; Pém, Attila

doi:10.1016/j.autcon.2013.04.004

Cited by 22 publications

(15 citation statements)

References 9 publications

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“…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%

“…A different version of the cumulative average is the moving average, which takes into account only recent data in the analysis (Everett and Farghal, 1997). The time frame for the data inclusion depends on the analyst's preference, leading either to the unit or cumulative data on each marginal situation (Mályusz and Pém, 2013). The integration of the most recent data and the previous average into one calculation results in the estimation of the exponentially weighted average.…”

Section: Data Representationmentioning

confidence: 99%

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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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Section: Data Representationmentioning

confidence: 99%

Section: Data Representationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The effect of experience on work efficiency was also surveyed in the construction industry, for example, in works related to reinforced concrete floors [13], roof coating [14], reinforced concrete piles [15] or installing rebars [16]. The learning curve was also used for planning motorway construction [17] as well as the installation of underwater caissons [18].…”

Section: The Learning-forgetting Effectmentioning

confidence: 99%

Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect

Rzepecki

Biruk

2018

MATEC Web Conf.

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Linear projects such as highways, multi-storey buildings or pipelines are implemented by repeating the sequences of processes on units. Repetitive operations affect acquiring more and more experience, which shortens the duration of subsequent activities. On the other hand, the possible breaks in work make this effect disappear. Breaks at work are most often caused by the lack of crew work continuity. While delaying the subsequent sequences of processes properly, it is possible to reduce interruptions and limit the forgetting effect. A simulation method for scheduling linear construction projects has been developed, taking into account the learning and forgetting effect. It allows to increase the probability of the crew work continuity while meeting the project deadline with a specific probability. As a result, the estimation of the project duration is more accurate, which may provide an advantage at the stage of submitting offers in tenders for construction works.

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“…Several researchers have suggested that Wright's model is the best model available for describing the future performance of repetitive work (Everett and Farghal, 1994;Couto and Texiera, 2005). In the exponential average method (Mályusz and Pém, 2013), α = 0.5 yielded the most accurate predictions. Of course, there is no consensus on which model provides the best fit and predictability for construction data (Srour et al, 2016).…”

Section: Learning Curvementioning

confidence: 99%

An Estimation of the Learning Curve Effect on Project Duration with Monte Carlo Simulation

Mályusz

Varga

2018

Period. Polytech. Arch.

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The aim of this paper is to estimate learning curve effect on project duration with the mean of project scheduling techniques. To measure this effect only one assumption is taken: the activity time individuals / groups take to perform an activity decreases at a given rate as experience is gained with the activity. Unfortunately this effect directly is not taken into account by project management software. In some software after scheduling, supervisor manually can switch on the "as soon as possible" or "as late as possible" buttons on an activity.Monte Carlo simulation was used to model the risks in the total project durations. It is assumed that the (normal) durations of the activities can vary according to the beta distribution. The minimum estimate is 95 % of the original (normal) duration, and the maximum estimate is 140 % of the original (normal) duration. We assumed that most likely value is the (normal) duration of each activity. The learning effect on project duration with the help of test problems and real problems was investigated. In test problems learning effect can occur between two consecutive activities. These pairs are chosen randomly. After calculating project duration, these pairs are allocated to be closer to each other using the predecessor's total float time. It is assumed that the duration of impending repetitive activities is shorter due to the learning curve effect if the gap between consecutive activities is small enough. This iteration is carried out until it is not possible to shorten the successor's activity time in a pair. It is shown that this effect brings a 2-3 % shorter project duration meanwhile variance is also left in a 1-2 % range. Numerical tests were implemented by XPRESS-Mosel Optimization Software.

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Prediction of the learning curve in roof insulation

Cited by 22 publications

References 9 publications

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

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

Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect

An Estimation of the Learning Curve Effect on Project Duration with Monte Carlo Simulation

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