Real-Time Activity Duration Extraction of Crane Works for Data-Driven Discrete Event Simulation

The construction industry is struggling with low productivity rates because of a low level of digitalization, dynamic interactions, and uncontrollable circumstances on sites, which make the planning process complex. Usage of the digital twin construction paradigm enables to facilitate construction management and leverage the sector's unexploited potential. This research addresses current shortcomings by real-time discrete event simulation. During crane operations, kinematic data were collected, which were classified by machine learning algorithms for activity recognition and duration extraction. Based on the identified durations, Goodness-of-Fit techniques determined suitable probability density functions. The resulting probability density functions were used as input parameters in stochastic discrete event simulations. It was shown that with enriched data collection, probability density functions have to be updated. The data-driven discrete event simulation facilitates decision-making processes by providing more reliable real-time information for the planning of upcoming construction works. Thus, data-based instead of experience-based management can be enabled.