F. Vahdatikhaki, *A. Hammad, and S. Setayeshgar

Concordia University

1455 De Maisonneuve Blvd. W

Montreal, Canada H3G 1M8

(*Corresponding author:

Simulation techniques have offered significant boosts toward a cost-and-time-optimized planning of construction projects by enabling project managers to effectively comprehend the behavior of projects. Using historic data from projects of like nature, simulation considers uncertainties involved in a project through accommodating the stochastic modeling parameters. However, the heavy reliance on the statistical data and not taking into account the context-specific features of the project cause the degradation in the realism and accuracy of the simulation models. Similarly, the extent to which a historic pattern could be retrofitted to new projects will decrease in line with the growing uniqueness of the projects and the novelty of construction methods. Furthermore, the existing real-time simulation frameworks are not capable of distinguishing the transient environmental changes, with minimal long-term impacts on the productivity, from the influential changes that will greatly impact an operation. In addition, existing simulation tools are devoid of location awareness, resulting in the inability to consider safety threats in their representation of the project. To address these issues, this research proposes a framework based on the integration of new tracking technologies used in Automated Machine Guidance (AMG) with simulation-driven 4D modeling methods. The proposed framework automates the adjustment of the simulation model based on the updated data from the site, and thus transforms simulation from a predictive tool used at the planning phase to a proactive monitoring platform usable throughout the planning and construction phases. A prototype is developed to test and demonstrate the effectiveness of the proposed approach.
Keywords: Simulations; Simulation; Model; Models; Data; Trucks; Truck; Operation; Construction;
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