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: 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=715584
• 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性是建筑行业的两大目标。目前的项目管理方法主要依靠人工现场观察和手动数据收集来分别检测潜在危险和进度监控。人工智能 (AI) 和传感技术的进步为经济、高效和准确的自动化数据收集提供了新的手段，这有望为许多行业带来革命性的变化。然而，由于建筑项目的复杂性和动态性，在建筑行业应用这些进步更具挑战性。为了充分利用这些进步，需要对每种技术的优点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。 该提案旨在利用网络物理系统方法提高建筑项目的生产力和安全性。该研究项目的长期目标是利用最先进的人工智能技术结合建筑信息模型（BIM）、近实时模拟和多代理系统，创造与提高建筑项目的生产力和安全性相关的知识，并最终实现智能建筑工地的概念。 所提出的方法有以下主要任务：（1）使用配备光探测和测距（LiDAR）扫描仪的无人机（UAV）更新现场模型以进行进度监测； （2）利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； (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意外问题造成的延误影响来对建筑业产生影响。由此产生的由实际施工进度数据校准的近实时模拟将为从业者提供有关其项目的准确可靠的信息，这将加强风险管理。预计该计划及其总部培训的成果将为加拿大建筑业带来基于技术创新的竞争优势。