Trajectory prediction of mobile construction resources toward pro-active struck-by hazard detection

D. Kim, M. Liu, S. Lee, V. R. Kamat

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations

Abstract

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 proactive intervention in hazardous situations. It will ultimately contribute to promoting a safer working environment for construction workers.

Original languageEnglish (US)
Pages982-988
Number of pages7
DOIs
StatePublished - 2019
Externally publishedYes
Event36th International Symposium on Automation and Robotics in Construction, ISARC 2019 - Banff, Canada
Duration: May 21 2019May 24 2019

Conference

Conference36th International Symposium on Automation and Robotics in Construction, ISARC 2019
Country/TerritoryCanada
CityBanff
Period5/21/195/24/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Building and Construction
  • Human-Computer Interaction

Keywords

  • Deep neural network
  • Hyper-parameter tuning
  • Pro-active intervention
  • Struck-by hazard
  • Trajectory prediction

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