Context-realistic virtual reality-based training simulators for asphalt operations

Asphalt operations are equipment-intensive, highly-coordinated, and context-sensitive. To ensure high-quality asphalt, operators need to be mindful of, among others, the degree of compaction required/achieved, temperature of the asphalt mixture, its cooling rate, other equipment, and the supply logistics. However, the current training program for the operators of asphalt equipment is inadequate because (1) the training heavily depends on the use of actual equipment for the training and because of the cost/safety risks involved in using actual equipment, novice trainees do not get enough opportunity to develop the required skills; and (2) given the sensitivity of the asphalt operations to the environment, the type of the asphalt mixture, logistics, etc., it is very difficult to allow trainees become sensitized to all the influential parameters in a limited time provided for the practical training. In recent years, Virtual Reality (VR) based training simulators are employed to help train operators in a safe environment. However, scenarios used in the construction simulators are mostly hypothetical. The context of operation in these scenarios is static and devoid of dynamism common in a construction site. This is a major oversight, particularly in highly-collaborative asphalt operations. Therefore, it seems crucial to better represent the actual work context in the training simulators. Given the myriad of parameters involved in the asphalt operations, designing a training scenario based on pure modeling is very challenging. This research proposes an approach for developing a training simulator based on the data collected from actual asphalt operations. The collected data will be analyzed and translated into a training simulator that can better capture the interaction between various operators of asphalt operations. A prototype is developed and a case study is conducted to demonstrate the feasibility of the proposed approach. It is shown that actual data can be used to effectively generate realistic training scenarios