Intelligent monitoring and data analytics for construction safety and productivity improvement

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Workers in construction are exposed to disproportionately high physical and health risks due to the labour-intensive nature of tasks and the unpredictable and uncontrollable nature of the working environment. These conditions contribute to an elevated risk of the onset of work-related musculoskeletal disorders and the occurrence of workplace lost-time injuries, thereby further hampering productivity. These issues remain a challenge due to the lack of a robust method for timely monitoring and control of these activities. Numerous efforts have been made to capture this information using wearable monitoring technologies and professional sensors. However, the broad implementation and scaling of these technologies is inhibited by the high cost of the sensors, as well as their adverse effect on ongoing construction activities. The aim of this project, then, is to develop a cost-effective and contactless solution for real-time monitoring of workers and the working environment during construction. The system will be developed based on advanced artificial intelligence and computer vision algorithms. Combining computer vision with advanced ergonomic and safety risk assessment methods, the system will be able to provide timely feedback and prediction regarding the unsafe behaviour of workers and the presence of hazards in construction workplaces. This information will benefit construction workers by reducing the risk to which they are exposed during work. It will benefit project owners and managers by giving them more control over the inherent risk in construction activities, and helping them to minimize the financial and productivity losses associated with incident-related injuries and machine failure.