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Traditional lectures have difficulties instilling pragmatic skills in construction engineering students due to the inability to illuminate the complexities within the human-robot collaborative construction environment. While on-site can acclimatize construction students to reality and construct knowledge that can solve safety challenges, it is challenging to organize on-site training trips owing to the dangerous nature of construction workplaces. This research aimed to explore virtual reality (VR) as a tool to enhance students’ perception and knowledge of construction robotic safety. For this purpose, the study developed a virtual training platform for providing construction engineering students with safety knowledge on interacting with simulated robots within the virtual environment of construction sites. A self-assessment approach was leveraged among 20 recruited students to demonstrate the efficacy of students’ engagement and learning outcomes from the proposed learning approach over the traditional learning approach. Results indicated a statistical difference in students’ learning outcomes and engagement levels between the developed approach and the traditional approach. Findings demonstrated the implications of VR as an experiential tool to enhance the students’ learning of robotic safety in construction. 

Our symmetry-free model for spectrum allocation (SA) in networks of general topology leverages two properties: (1) SA is equivalent to a connection permutation problem, and (2) in assigning spectrum, it is sufficient to consider the allocation made by the first-fit (FF) algorithm. This model opens up algorithmic approaches that altogether sidestep spectrum symmetry, i.e., eliminate from consideration the exponential number of equivalent solutions resulting from spectrum slot permutations. Recursive FF (RFF) is such an algorithm; it applies FF recursively to search the connection permutation space and solve the SA problem optimally. Moreover, parallelism is inherent in the spectrum symmetry-free model, as the connection permutation space may be naturally decomposed into non-overlapping subsets that can be searched independently. Accordingly, RFF admits multi-threaded implementations that may be tailored to the computing environment at hand. In this work, we present two strategies for parallelizing the execution of RFF, and we evaluate them experimentally using a comprehensive set of metrics. Our experiments indicate that RFF explores a vast number of symmetry-free solutions, and for moderate-sized networks, it takes mere seconds to yield solutions that are either optimal or very close to the lower bound.