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.

建筑工程的生产率和安全性是建筑业的两大目标。目前的项目管理方法主要依靠人工现场观察和人工数据收集，分别检测潜在的危险和监测进度。人工智能(AI)和传感技术的进步为负担得起、高效和准确的自动化数据收集提供了新的手段，预计这将有助于许多行业的革命性变化。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，需要科学地了解每种技术的优势和局限性，然后开发创新的解决方案，以应对建筑业的特定需求和挑战。这项建议旨在利用网络-物理系统方法提高建筑项目的生产率和安全性。这项研究计划的长期目标是利用最先进的人工智能技术，结合建筑信息建模(BIM)、近实时模拟和多代理系统，创造与提高建筑项目生产率和安全性相关的知识，并最终实现智能建筑工地的概念。该方法的主要任务如下：(1)使用配备光探测和测距(LiDAR)扫描仪的无人机(UAV)更新工地进度监测模型；(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模拟模型开发建筑项目的实时数字孪生模型；以及(4)使用多代理系统支持施工工地的决策。拟议的研究计划预计将通过提高生产率和安全，并减少因建筑工地上不可预见的问题而造成的延误的影响，对建筑业产生影响。由此产生的由实际施工进度数据校准的近乎实时的模拟将为从业者提供关于其项目的准确和可靠的信息，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。