A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Historical Accident and Injury Database-Driven Audio-Based Autonomous Construction Safety Surveillance
Construction safety has always been one of critical concerns in the construction industry with diverse approaches and technology for consistently managing safety hazard issues being examined and adopted. However, the current technology such as a vision-based approach does not appear to fully support consistent and robust safety monitoring because of its heavy-data processing and inherent restrictions including limited angle coverage and view detectability. A vulnerable construction environment requires an advanced safety surveillance and event detection approach. To provide a supplement safety monitoring method, this project has an objective to establish an audio-based autonomous safety surveillance system with alarming pre-notifications and map visualization based on identified work activities and potential safety issues. This proposed system adopts a schedule-based sound data training method and incorporates historical occupational injury and illness manual data with classified sources and events of accidents in each construction activity. The injury data are informed by a daily project schedule to link potential safety hazard to daily planned work activities. By applying a machine learning technique, the system accurately categorizes a sound type according to sound training data scope-downed by project schedule and safety data and provides pre-warnings in accordance with any detected irregular events. The system is expected to contribute to the body of knowledge for safety monitoring and provide the potential to be integrated into a robust automated safety surveillance system with occupational accident data and significantly improving construction activities classification accuracy.
Historical Accident and Injury Database-Driven Audio-Based Autonomous Construction Safety Surveillance
Construction safety has always been one of critical concerns in the construction industry with diverse approaches and technology for consistently managing safety hazard issues being examined and adopted. However, the current technology such as a vision-based approach does not appear to fully support consistent and robust safety monitoring because of its heavy-data processing and inherent restrictions including limited angle coverage and view detectability. A vulnerable construction environment requires an advanced safety surveillance and event detection approach. To provide a supplement safety monitoring method, this project has an objective to establish an audio-based autonomous safety surveillance system with alarming pre-notifications and map visualization based on identified work activities and potential safety issues. This proposed system adopts a schedule-based sound data training method and incorporates historical occupational injury and illness manual data with classified sources and events of accidents in each construction activity. The injury data are informed by a daily project schedule to link potential safety hazard to daily planned work activities. By applying a machine learning technique, the system accurately categorizes a sound type according to sound training data scope-downed by project schedule and safety data and provides pre-warnings in accordance with any detected irregular events. The system is expected to contribute to the body of knowledge for safety monitoring and provide the potential to be integrated into a robust automated safety surveillance system with occupational accident data and significantly improving construction activities classification accuracy.
Historical Accident and Injury Database-Driven Audio-Based Autonomous Construction Safety Surveillance
Xie, Yiyi (author) / Lee, Yong-Cheol (author) / Shariatfar, Moeid (author) / Zhang, Zhongjie "Doc" (author) / Rashidi, Abbas (author) / Lee, Hyun Woo (author)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
Computing in Civil Engineering 2019 ; 105-113
2019-06-13
Conference paper
Electronic Resource
English
| British Library Conference Proceedings | 2019
Safety Analysis and Management Using the Past Accident Database
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