Intelligent monitoring and data analytics for construction safety and productivity improvement

智能监控和数据分析，提高施工安全和生产力

• 批准号: 561120-2020
• 负责人: Li, Xinming
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706991
• 关键词: Intelligent; monitoring; data; analytics; construction

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.

由于工作的劳动密集性以及工作环境的不可预测和不可控制性，建筑工人面临的身体和健康风险过高。这些条件增加了患与工作有关的肌肉骨骼疾病和发生工作场所损失时间伤害的风险，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强大方法，这些问题仍然是一个挑战。已经做出了许多努力来使用可穿戴监测技术和专业传感器捕获这些信息。然而，这些技术的广泛实施和扩展受到传感器的高成本以及它们对正在进行的施工活动的不利影响的抑制。因此，该项目的目的是开发一种具有成本效益的非接触式解决方案，用于在施工期间实时监控工人和工作环境。该系统将基于先进的人工智能和计算机视觉算法开发。该系统将计算机视觉与先进的人体工程学和安全风险评估方法相结合，能够及时反馈和预测工人的不安全行为以及建筑工作场所存在的危险。这些信息将有利于建筑工人，减少他们在工作期间面临的风险。它将使项目业主和管理人员受益，使他们能够更好地控制施工活动中的固有风险，并帮助他们最大限度地减少与事故相关的伤害和机器故障相关的财务和生产力损失。