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As a proactive means of preventing struck-by accidents in construction, many studies have presented proximity monitoring applications using wireless sensors (e.g., RFID, UWB, and GPS) or computer vision methods. Most prior research has emphasized proximity detection rather than prediction. However, prediction can be more effective and important for contact-driven accident prevention, particularly given that the sooner workers (e.g., equipment operators and workers on foot) are informed of their proximity to each other, the more likely they are to avoid the impending collision. In earlier studies, the authors presented a trajectory prediction method leveraging a deep neural network to examine the feasibility of proximity prediction in real-world applications. In this study, we enhance the existing trajectory prediction accuracy. Specifically, we improve the trajectory prediction model by tuning its pre-trained weight parameters with construction data. Moreover, inherent movement-driven post-processing algorithm is developed to refine the trajectory prediction of a target in accordance with its inherent movement patterns such as the final position, predominant direction, and average velocity. In a test on real-site operations data, the proposed approach demonstrates the improvement in accuracy: for 5.28 seconds’ prediction, it achieves 0.39 meter average displacement error, improved by 51.43% as compared with the previous one (0.84 meters). The improved trajectory prediction method can support to predict potential contact-driven hazards in advance, which can allow for prompt feedback (e.g., visible, acoustic, and vibration alarms) to equipment operators and workers on foot. The proactive intervention can lead the workers to take prompt evasive action, thereby reducing the chance of an impending collision. 

Effective construction engineering and management education requires hands-on experiences that have not traditionally been offered in classroom settings. Physical building competitions like Solar Decathlon are valuable for providing experiential learning opportunities that may support tacit and explicit knowledge development among students, but they are often not available to all students due to funding and resource limitations. Less resource intensive teaching strategies, such as project based learning, can mimic the benefit of physical experiences by providing context to learning content. This paper reviews project based learning literature to identify trends in reported learning gains from the adoption of this strategy. Additionally, emerging technologies offer the ability to create low cost, immersive multimedia environments that may be able to support the types of learning targeted by physical design and construction experiences. Literature on multimedia learning theory is explored to identify opportunities for multimedia applications to facilitate learnings derived by physical educational contexts, but with the use of increasingly affordable multimedia strategies. This paper resulted in identifying six learning gains that have a theoretical potential to be facilitated using augmented reality and virtual reality technologies. The theoretical potential was deduced based on prior research on teaching strategies that provide real-world context to learning content. The authors of this paper propose using the identified learning gains as targets to specifically design implementation studies to verify this potential. The learning gains identified in the results section can be targeted and measured in future research when empirically validating the use of immersive technologies for construction education. The contribution of this work is in synthesizing the learning gains that future researchers should target based on evidence from prior research in related learning contexts.