Understanding labour productivity in reinforcement works through real-life data analysis and simulation

Abstract

Labour productivity in construction exhibits significant variability, which poses challenges for reliable estimation of task duration. This study proposes a probabilistic framework for modelling labour productivity and execution time in reinforcement works, based on empirical data and Monte Carlo simulation. Labour productivity is modelled using fitted continuous probability distributions, while crew size is represented as a discrete empirical variable. The analysis is conducted separately for different types of structural elements to avoid aggregation bias. The results show that productivity varies not only in magnitude but also in distributional characteristics
across element types. The apparent relationship between productivity and crew size is shown to result primarily from data aggregation rather than a direct dependency. Simulation outcomes indicate that execution time
distributions are asymmetric, with higher-percentile values significantly exceeding median estimates, reflecting substantial schedule risk. The proposed approach provides a more realistic basis for construction duration estimation under uncertainty and supports improved planning and decision-making by explicitly accounting for
variability and heterogeneity in construction processes.