In this paper, we present a virtual learning environment for an industrial assembly task, which combines an easyto-use interaction with an intuitive user experience. It is shown that such a virtual environment can be used for initial training to introduce tasks to new employees, but also experts may benefit from advanced training in case of new products, or new assembly routines. Consequently, this application was validated twice. First, in a lab pilot study showing that the simple interactions and helpful instructions were appreciated by the participants. Second, professionals from industry were asked to perform the task and to evaluate the usefulness of the virtual learning environment considering its industrial applicability. Based on the achieved scores in common evaluative questionnaires and the post-study interviews, both, the lab pilot and the industrial study, have performed well and will be further developed in close collaboration with the industrial partner.

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Due to current trends in the manufacturing industry, such as mass customization, manual operations contribute drastically to the overall costs of a product. Methods-Time-Measurement (MTM) identifies the optimization potential of manual workplaces, which significantly influences a worker’s productivity. However, traditional MTM requires great efforts to observe and transcribe manual assembly processes. Yet, various digital approaches exist that facilitate MTM analyses. While most of these approaches require the existence of real workplaces or cardboard mock-ups, it would be beneficial to conduct a virtual MTM in earlier phases of production planning. However, the quality of virtual MTM analyses compared to traditional MTM conducted in reality has not been assessed yet. This paper is addressing it by conducting a comparative user study with 21 participants completing the same task both at a real and virtual workplace, which they access via virtual reality technology. Our results show that participants’ MTM-2 values achieved at the VR workplace are comparable to those at the real workplace. However, time study data reveals that participants moved considerably slower in VR and thus needed more time to accomplish the task. Consequently, for the measurement of manual work in VR, it is even necessary to utilize predetermined times, such as MTM-2 since time study data is insufficient. This paper also serves as a proof of concept for future studies, investigating automated transcription systems that would further decrease the efforts conducting MTM analyses.

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Interest in the field of data analytics among researchers and practitioners has been rising over the last few years. The digitalization of the built environment leads to increased availability of data, enabling the introduction of data analytics. In this paper we propose a novel framework for data driven value creation in architecture, engineering and construction (AEC). The framework consists of four value creating categories, which are mapped on a building’s lifecycle. Additionally, we analyse over ten data analytics applications by the value they create along the building lifecycle. The paper concludes by suggesting future research for data analytics in AEC.

One of the main characteristics of virtual reality (VR) is immersion, which leads to comprehensive illusions of reality. Accordingly, VR is used in many applications like entertainment, marketing, and training. Especially in training applications, the effect of immersion on training success is still not entirely clear, since too much immersion may cause side effects such as users experiencing high mental demand whereas too little may disturb users' well-being. To further investigate the matter, we developed two virtual training environments, wherein users train a typical industrial assembly task either in low or high immersive VR. In a controlled pilot study, we additionally introduced a third condition, the control group, which justifies the necessity of the training. Immediately after the VR training session, each participant completed the corresponding real assembly task in which their performance was measured. Preliminary results from our pilot study show that participants trained in high immersive VR performed better, while negative side effects could not be detected.

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Inferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.