Publications / 2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada

Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection

Daeho Kim, Meiyin Liu, Sanghyun Lee and Vineet R. Kamat
Pages 982-988 (2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada)

In construction, unanticipated struck-by hazards often arise, which have resulted in a significant number of construction fatalities. To address this problem, many studies have attempted to automate proximity monitoring and struck-by hazard detection using various technologies, such as wireless sensors and computer vision methods. While this technology focuses on understanding what is happening as hazards arise, it is not equipped to detect future hazards. In impending situations, detecting current hazards may not provide enough time for workers to take evasive actions. To address this challenge this study develops a trajectory prediction model for mobile construction resources. Specifically, this study conducts hyper-parameter tuning of a deep neural network, called Social Generative Adversarial Network to develop a prediction model capable of predicting more than five seconds. Further, a test on a real construction operations data follows to validate developed models’ trajectory prediction accuracy. As a result, a developed model could achieve promising accuracy: the average displacement error and the final displacement error were 0.78 and 1.27 meters, respectively. The trajectory prediction allows for detecting future hazards, which will support pro-active intervention in hazardous situations. It will ultimately contribute to promoting a safer working environment for construction workers.

Keywords: Struck-by hazard; Pro-active intervention; Trajectory prediction; Deep neural network; Hyper-parameter tuning