Learning Curve Predictors for Construction Field Operations

Abstract

Many repetitive construction field operations exhibit a learning curve, over which the time or cost per cycle decreases as the cycle number increases. This paper evaluates several mathematical models to determine which best describes the relationship between the activity time or cost and the cycle number. For completed activities, cubic learning curve models are found to provide the most reliable statistical fit, and linear models provide the least reliable fit. The real potential value of learning curves is their ability to predict the time or cost needed to perform future activities. This paper presents a methodology for predicting future activity time or cost based on completed activity data. The best predictors of future performance are found to be linear models. The cubic models that best describe completed activities are poor predictors of future performance.