Intelligent monitoring and data analytics for construction safety and productivity improvement

由于任务的劳动密集性以及工作环境的不可预见性和不可控制性，建筑工人面临着极高的身体和健康风险。这些情况增加了发生与工作有关的肌肉骨骼疾病和发生工作场所工伤的风险，从而进一步阻碍了生产力。由于缺乏及时监测和控制这些活动的有力方法，这些问题仍然是一项挑战。使用可穿戴式监控技术和专业传感器，已经做出了许多努力来捕捉这些信息。然而，传感器的高昂成本及其对正在进行的建筑活动的不利影响阻碍了这些技术的广泛实施和推广。因此，该项目的目的是开发一种经济高效的非接触式解决方案，用于实时监控施工期间的工人和工作环境。该系统将基于先进的人工智能和计算机视觉算法开发。该系统将电脑视觉与先进的人体工程学和安全风险评估方法相结合，将能够就工人的不安全行为和建筑工作场所存在的危险及时提供反馈和预测。这些信息将减少建筑工人在工作期间面临的风险，从而使他们受益。它将使项目业主和经理更好地控制建筑活动中的固有风险，并帮助他们将与事故相关的伤害和机器故障相关的财务和生产力损失降至最低。

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.