Learning Curve Models of Construction Productivity

TLDR

Learning curve models are being studied to explain construction productivity patterns. The study applied five mathematical learning‑curve models to 65 data sets of unit rates and used time data from 466 precast concrete floor planks to evaluate their predictive reliability. The analysis shows that a cubic model best predicts unit rates, whereas the commonly used straight‑line model is only marginally adequate and unreliable because the learning rate varies.

Abstract

Current research into learning curve models of construction productivity is presented. Five mathematical models are identified and each of these are used to model unit rates for 65 sets of data. The correlation between predicted and actual unit rates is determined, and on this basis, it is concluded that the best predictor is a cubic model. The often cited straight‐line model is only marginally adequate. The validity of the straight‐line model is further undermined by showing that the learning rate is not a constant value. Time data for erecting and setting 466 precast concrete floor planks is used to support the conclusion that the straight‐line model is not a reliable model for predicting future performance.