Managing Planning Uncertainty in 3D-Printed Rural Housing Projects Using Monte Carlo Simulation
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Abstract
The use of emerging technologies, such as three-dimensional (3D) printing, in rural housing projects presents an opportunity to transform traditional construction practices, which are often costly, slow and unsustainable. However, the implementation of these technologies faces significant challenges related to uncertainty in planning and execution, particularly because of the lack of historical data and the variability of construction processes. This study evaluates the applicability of Monte Carlo simulation (MCS) as a tool for modelling the duration of a pilot 3D-printed rural housing project, considering both the inherent uncertainty of emerging technologies and the risks associated with external and operational factors. Based on the identification of critical activities and analysis of their probabilistic distributions, the schedule developed using the critical path method (CPM) with a deterministic duration of 56 days has less than 28.7% probability of being met. However, the probability that the actual project duration will not exceed 60 days is estimated at 80%. Additionally, the study identified how fluctuations in activity duration directly affect productivity and increase operational risk, thereby providing valuable insights into improving planning and risk management in similar projects. The results obtained not only help reduce uncertainty in decision-making in similar contexts but also establish a methodological foundation for future research on the effective integration of 3D printing technologies in rural settings. Thus, this study contributes to closing a critical gap in the management of innovative construction projects in regions that face logistical, infrastructural and technological implementation challenges.
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