Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors

Construction sites need to be monitored continuously to detect unsafe conditions and protect workers from potential injuries and fatal accidents. In current practices, construction-safety monitoring relies heavily on manual observation, which is labor-intensive and error-prone. Due to the complex environment of construction sites, it is extremely challenging for safety inspectors to continuously monitor and manually identify all incidents that may expose workers to safety risks. There exist many research efforts applying sensing technologies to construction sites to reduce the manual efforts associated with construction-safety monitoring. However, several bottlenecks are identified in applying these technologies to the onsite safety monitoring process, including (1) recognition and registration of potential hazards, (2) real-time detection of unsafe incidents, and (3) reporting and sharing of the detected incidents with relevant participants in a timely manner. The objective of this study was to create and evaluate a low-cost automated safety monitoring system to assist in the construction-safety monitoring process. This paper presents a framework for this safety monitoring system as a cloud-based real time on-site application. The system integrates Bluetooth low-energy (BLE)-based location detection technology, building information model (BIM)-based hazard identification, and a cloud-based communication platform. Potential unsafe areas are defined automatically or manually in a BIM model. Real-time worker locations are acquired to detect incidents where workers are exposed to predefined risks. Then, the safety monitoring results are instantly communicated over the cloud for effective safety management. Through a real-world construction site case study, the system successfully demonstrated the capability to detect unsafe conditions and collect and analyze the trajectories of workers with respect to potential safety hazards. The results indicate that the proposed approach can assist in the construction site monitoring process and potentially improve site safety.