Workers in offsite construction facilities are often exposed to repetitive motion and awkward body postures that are associated with the risk of developing work-related musculoskeletal disorders despite the use of automated equipment on production lines. To reduce the exposure to these risks, an investigation of the physical demands that workstations impose on workers’ bodies is needed. Since traditional methods used to collect human body motions have limitations, such as workplace interruptions and biased results due to subjective observation, this paper proposes a virtual reality (VR)–motion capture (MOCAP)-based ergonomic assessment method to evaluate ergonomic risks in a laboratory setting during the design phase of workstation development. It is expected that the number of iterations of physical workstation prototypes would be reduced if ergonomic risk ratings are identified proactively in the initial phases of workstation design, which would thereby reduce the cost and time required to develop and implement an improved workstation design. The present study includes a feasibility analysis of the proposed method in which participants representative of specific percentiles of the population based on their physical stature were invited to voluntarily participate in a research experiment. The results obtained demonstrate that the proposed method can successfully simulate the elemental motions, referred to as therbligs, of reaching and positioning (Pearson’s correlation coefficient is found to equal 0.80 and 0.94, respectively), while the simulation of the assembling therblig requires further investigation. The contribution of this study is a virtual reality–motion captured-enabled ergonomic risk assessment method applied to workstation design for offsite construction production lines. In addition, the deployment of the proposed method allows a holistic ergonomic assessment that considers objective and subjective parameters.

The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers’ trajectory prediction is required. With recent technology advancements, the study in the area of trajectory prediction has benefited from various data-driven approaches. However, existing data-driven approaches are mostly limited to considering only the movement information of workers in the workplace, resulting in poor estimation accuracy. In this study, we propose an environment-aware worker trajectory prediction framework based on long short-term memory (LSTM) network to not only take the individual movement into account but also the surrounding information to fully exploit the context in the modular construction facilities. By incorporating worker-to-worker interactions as well as environment-to-worker interactions into our prediction model, a sequence of the worker’s future positions can be predicted. Extensive numerical tests on synthetic as well as modular construction datasets show the improved prediction performance of the proposed approach in comparison to several state-of-the-art alternatives. This study offers a systematic and flexible framework to incorporate rich contextual information into the prediction model in modular construction. The observation of how to integrate construction data analytics into a single framework could be inspiring for further future research to support robust construction safety practices.

Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been... Practical ApplicationsAs the market share of OSC techniques continues to grow, it is vital for construction professionals to stay up to date with the latest advances and technologies so that they a...