Critical investigation into the applicability of the learning curve theory to rebar fixing labor productivity

The learning phenomenon has proved applicable in various industries, especially those associated with mass productions such as aircraft and automobile assembly. The learning process in labor-intensive industries is assumed to be more significant in the sense that automated work is constrained by the fact that machines cannot benefit from previous experience and therefore would not "learn" to run any faster or increase the rate of production. Several previous investigations into the applicability of learning curve theory to the construction industry proved the importance of this concept to labor productivity. Nonetheless, a thorough examination of the literature revealed a dearth of research into the effect of learning on the rebar fixing trade. Therefore, the objective of this research is to explore the influence of recurring building floor configurations on rebar fixing labor productivity. To achieve this objective, fixing labor inputs of beams and slabs from 21 multistorey residential building floors were collected and analyzed using the straight-line learning curve model. Fixing labor inputs for each cycle, i.e., floor, and its associated cycle number were modeled using the least-squares method. According to the learning curve theory, labor inputs are expected to decrease by a certain percentage as the cycle number increases. Contrary to the basic principle underlying the theory, the majority of buildings observed exhibited either an increase or a negligible reduction in labor inputs as the cycle number of recurring floors increased.