D4.4 – Proactive Right-time Risk Monitoring and Detection Methods v2
Creators: name: Teizer, Jochen
name: Speiser, Kilian
Publisher: name: Zenodo
Year: 2022

description: The COGITO Deliverable D4.4 “Proactive Real-time Risk Monitoring and Detection Methods v2” aims at documenting the state-of-the-art regarding the existing methods for enhancing safety in construction, focusing primarily on areas relevant in the context of COGITO. This report includes: (a) the need for right-time safety in the construction phase and its state-of-the-art, (b) the definition of a close call and the proposed close call reporting process, (c) the sensor-based methods for close call data collection and risk monitoring and detection, (d) the methods for intelligent close call data analysis and reporting, and (e) the tools for accident prediction in regards to heavy construction equipment and pedestrian workers in the realistic, complex, and dynamic construction environments. This deliverable also documents the current version of the developed Proactive Real-Time Risk Monitoring and Detection application called ProActiveSafety. In addition, it reports on the iteration of the development activities within the COGITO Task T4.2 “Proactive Real-time Risk Monitoring and Detection”.

In summary, due to the evolving workspace typically found at construction sites, hazards can emerge dynamically. Visibility-related fatalities constitute more than half of the fatal occupational accidents in construction, caused by workers being in a blind zone of heavy equipment or not being seen due to obstructions. To promote safety, research has been done on (a) enhancing the existing manual close call reporting process and (b) developing close call data gathering and analysis, and proactive warning systems to notify of dangers and to alert pedestrian workers or operators when immediate attention or action is required to prevent an accident. The proactive warning systems often rely on sensors to detect the distance between moving heavy machinery and pedestrian workers or obstacles. Several types of sensor-based methods for risk monitoring and detection exist and are analysed in depth in this report. For instance, camera-based, ultrasonic or radio systems are some of those. Three categories of input data were found to be used for trajectory data gathering in the construction literature: vision-based data, raw location tracking data, and 3-dimensional point cloud data from drone sensors. Close call data gathering and analysis, and furthermore, trajectory prediction in construction often refers to post- or run-time data processing, respectively. While post-processing close call data is very suitable for safety officers interested in infrequent safety status updates, run-time data processing focuses on the short-term prediction of a moving resource’s future paths (e.g., worker, equipment), i.e., within 1 to 10 seconds ahead for accident avoidance.

The Proactive Real-time Risk Monitoring and Detection application, called ProActiveSafety, consists of four modules, namely (a) the data analysis module, (b) the trajectory prediction module, (c) the hazard zones checking module and (d) the user interface. ProActiveSafety enables the Health and Safety (H&S) digital twin to predict hazardous situations (e.g., through the generation of risk heat maps or probability density calculations) based on state-of-the-art machine learning techniques on up-to-date near-real-time data queried from the digital twin platform. To this end, sample location tracking data have been utilised to train a type of artificial Recurrent Neural Network (RNN) called LSTM network which performs short-term proactive monitoring of hazards affecting moving workers and equipment in the dynamic construction environment. The future development will focus on the risk analysis module and will integrate construction semantic information (e.g., construction site layout plans) and hazard zones checking to further enhance the safety analysis.