Cyber-Physical System Approach for Improving Productivity and Safety of Construction Projects

提高建设项目生产力和安全性的网络物理系统方法

• 批准号: RGPIN-2019-05508
• 负责人: Hammad, Amin
• 金额: $ 4.52万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750647
• 关键词: Cyber; Physical; System; Approach; Improving

The productivity and safety of construction projects are two major goals of the construction industry. Current project management approaches mainly depends on human site observations and manual data collection for detecting potential hazards and progress monitoring, respectively. Advancements in Artificial Intelligence (AI) and sensing technologies are providing new means for affordable, efficient and accurate automated data collection, which is expected to contribute to revolutionizing many industries. However, because of the complexity and the dynamic nature of construction projects, applying these advancements in the construction industry is more challenging. In order to take full advantage of these advancements, there is a need to have scientific understanding of the advantages and limitations of each technology, and then to develop innovative solutions to address the specific needs and challenges of the construction industry. This proposal aims to improve productivity and safety of construction projects using a cyber-physical system approach. The long-term objective of this research program is the knowledge creation related to improving productivity and safety of construction projects using the state-of-the-art AI techniques combined with Building Information Modeling (BIM), near real-time simulation and multi-agent systems, and ultimately realizing the concept of the Smart Construction Site. The proposed methodology has the following main tasks: (1) Updating the model of the site for progress monitoring using an Unmanned Aerial Vehicle (UAV) equipped with a Light Detection and Ranging (LiDAR) scanner; (2) Recognizing the activities of workers and equipment using an advanced computer vision method; (3) Developing real-time digital twin of the construction project based on a detailed 4D simulation model; and (4) Supporting decision-making on construction sites using a multi-agent system. The proposed research program is expected to have an impact on the construction industry by improving productivity and safety and reducing the effect of delays caused by unforeseen problems on construction sites. The resulting near real-time simulation calibrated by actual construction progress data will provide practitioners with accurate and reliable information about their projects, which will enhance risk management. It is expected that the results of this program and its HQP training will give the Canadian construction sector a competitive advantage based on technological innovations.

建筑项目的生产率和安全是建筑业的两个主要目标。目前的项目管理方法主要依赖于人类现场观察和人工数据收集，分别用于检测潜在危害和进度监控。人工智能（AI）和传感技术的进步为经济、高效和准确的自动数据收集提供了新的手段，预计将有助于许多行业的革命。然而，由于建筑项目的复杂性和动态性，将这些进步应用于建筑行业更具挑战性。为了充分利用这些进步，需要对每种技术的优势和局限性有科学的了解，然后开发创新的解决方案来解决建筑行业的特定需求和挑战。 该提案旨在使用网络物理系统方法提高建筑项目的生产力和安全性。该研究计划的长期目标是使用最先进的人工智能技术与建筑信息建模（BIM），近实时仿真和多代理系统相结合，提高建筑项目的生产力和安全性，并最终实现智能建筑工地的概念。所提出的方法具有以下主要任务：（1）使用配备有光探测和测距（LiDAR）扫描仪的无人机（UAV）更新现场模型以进行进度监控;（2）使用先进的计算机视觉方法识别工人和设备的活动;（3）基于详细的4D仿真模型开发建设项目的实时数字孪生模型;（4）利用多智能体系统支持施工现场决策。 拟议的研究计划预计将通过提高生产率和安全性以及减少建筑工地上不可预见的问题造成的延误影响，对建筑业产生影响。通过实际施工进度数据校准的近实时模拟将为从业人员提供有关其项目的准确可靠信息，这将加强风险管理。预计该计划及其HQP培训的结果将使加拿大建筑业在技术创新的基础上获得竞争优势。