Digitization and Data Analytics in Off-site Construction

场外施工的数字化和数据分析

• 批准号: RGPIN-2020-04126
• 负责人: Lei, Zhen
• 金额: $ 5.17万
• 依托单位: University of New Brunswick
• 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=749376
• 关键词: Digitization; Data; Analytics; Off; site

As an alternative approach to traditional stick-built construction methods, the off-site construction approach allows construction to be performed within a controlled environment, with components prefabricated and assembled into modules and units and then transported to construction sites for installation. However, as the traditional approach is replaced by off-site construction, construction data has become scattered throughout the supply chain, resulting in complex analytics. At this juncture, a new cognitive paradigm is required to understand the implementation of emerging data analytics techniques for offsite construction. In the manufacturing industry, rapid advancement of Industry 4.0 technologies, including cloud computing and the Internet of Things (IoT), has enabled in-depth data analysis through real-time data exchange. Machine learning and artificial intelligence (AI) techniques make it possible for autonomous decision-making such as proactive machine control and predictive maintenance. These advancements have disrupted manufacturing planning systems and human-machine interaction mechanisms. The digitization of the physical environment is often referred to as digital twinning, where seamless data exchange and decision-making protocols function simultaneously with industrial processes to create integrated cyber-physical systems. Recognizing the impact of digitization in the manufacturing industry, this research program will scientifically develop a framework to digitize the offsite construction supply chain and provide intelligent decision support tools for the construction industry. I plan to develop digital models (digital twins) for manufacturing facilities, transportation and delivery networks, and on-site construction assembly processes. Various decision-making protocols (namely predictive, simulation, and optimization models) will be tested and implemented in various applications. Through an immersive reality environment, I will explore how data-driven decisions can be translated to intuitive presentations to end-users in order to validate and improve decision support systems. The development of this research program will have a wide-reaching impact on construction project stakeholders by providing timely and accurate decision-making mechanisms. This will help boost construction productivity and, consequently, increase profitability of the Canadian construction industry. This research program will provide industry-engaging and cross-disciplinary training opportunities for Highly Qualified Personnel (HQP) at graduate and undergraduate levels. I will encourage and strive for Equity, Diversity and Inclusion (EDI) in my research program. The HQP will work closely with construction industry partners, collaborate with other research institutions/departments, and be exposed to interdisciplinary research topics and training.

作为传统建筑施工方法的替代方法，场外施工方法允许在受控环境中进行施工，将组件预制并组装成模块和单元，然后运输到施工现场进行安装。然而，随着传统方法被非现场施工所取代，施工数据已分散在整个供应链中，导致分析变得复杂。在这个时刻，需要一种新的认知范式来理解新兴数据分析技术在场外施工中的实施。在制造业，工业4.0技术的快速发展，包括云计算和物联网（IoT），通过实时数据交换实现了深入的数据分析。机器学习和人工智能（AI）技术使自主决策成为可能，例如主动机器控制和预测性维护。这些进步扰乱了制造规划系统和人机交互机制。物理环境的数字化通常被称为数字孪生，其中无缝数据交换和决策协议与工业流程同时发挥作用，以创建集成的网络物理系统。 认识到数字化对制造业的影响，该研究计划将科学地开发一个框架，以优化非现场建筑供应链，并为建筑业提供智能决策支持工具。我计划为制造设施、运输和交付网络以及现场施工装配流程开发数字模型（数字孪生模型）。各种决策协议（即预测，模拟和优化模型）将在各种应用程序中进行测试和实施。通过沉浸式现实环境，我将探索如何将数据驱动的决策转化为直观的演示文稿，以验证和改进决策支持系统。该研究项目的发展将通过提供及时准确的决策机制，对建设项目利益相关者产生广泛的影响。这将有助于提高建筑生产力，从而提高加拿大建筑业的盈利能力。 该研究计划将为研究生和本科生层次的高素质人才（HQP）提供行业参与和跨学科的培训机会。我将鼓励和争取公平，多样性和包容性（EDI）在我的研究计划。HQP将与建筑行业合作伙伴密切合作，与其他研究机构/部门合作，并接触跨学科的研究课题和培训。