Big Data and Machine Learning-enabled Automated BIM for Projects (Auto-BIM): A Common Data Collaborative System for Improved Project Performance

支持大数据和机器学习的项目自动化 BIM (Auto-BIM)：用于提高项目绩效的通用数据协作系统

• 批准号: 104796
• 金额: $ 77.58万
• 依托单位: BALFOUR BEATTY PLC
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=104796
• 关键词: Big; Data; Machine; Learning; enabled

"BIM is touted as an effective way of addressing issues affecting the productivity of the construction industry. The task-force on BIM hypothesises that with BIM-adoption, ""significant improvement in cost, value and carbon-performance can be achieved through the use of open-sharable asset-information"". To reinforce its benefits, the government Construction-2025 lists BIM as a key-element for achieving its goal of 33% lower-cost, 50% faster-delivery, 50% lower-emissions and 50% improvement in export.Although there has been an increase in BIM-adoption, companies still find it difficult to implement the ""real"" BIM and realise the expected benefits. This is because of the naming convention in line with PAS-1192 and the need for adequate building-information to accompany 3D-representation of building materials/elements/products in a collaborative environment. For organisations that have surpassed the barrier to BIM-adoption, the main-challenge remains getting everyone involved in collaborative-projects to use CDE and to ascertain the exact-level of(and the specific) information required for different aspects and types of assets. Thus, some projects on which BIM is claimed to be used have only assembled digital information without providing useful information for construction, in the short-term, and data for asset-management in the long-term.Notwithstanding these challenges, there is currently no tool to support organisational BIM-adoption and compliance with the standard, leverage previous project lessons/historic data, support automated Construction-Operations-Building-Information-Exchange-(COBie) and facilitate supply-chain integration with product-manufacturers. Based on these, the project adopts techniques in Machine-Learning-(ML) and Big-Data-Analytics to create an innovative tool-(Auto-BIM) as a plug-in to BIM-tools. It consists of four-elements as follows:1\.**Automated-Naming-of-BIM-model-in-a-CDE-approach(Auto-BIMName)--**This helps project team to name their files in consistency/compliance with PAS-1192 and BS-EN-ISO-19650\. It would also help in automatically mapping the title-block, which is currently being done manually between collaborating companies/originators and roles.2\.**Automated-Population-of-Building-Information(Auto-BIMPopulate)--**This will prepopulate the 3D-representation of products/elements with relevant metadata including the Omniclass classification, model number, service information, materials, etc. This will facilitate a conventional approach to project communication/collaboration, and accelerate BIM-adoption and benefit-realisation.3\. **Automated-Sharing-of-BIM-Objects-and-Model-Data****(Auto-BIMShare)--**The Auto-BIMShare provides a unique platform for sharing reusable object library and associated information to facilitate common-language across software boundaries. It will also provide opportunities for manufacturers to make their products/materials available for potential specifiers and buyers. The Auto-BIMShare would facilitate co-creation/sharing of information between the design, procurement, and maintenance/operation team within/across projects4\.**Automated-BIM-learning-Platform(Auto-BIMLearn)--**BIM currently has no capacity for diagnosing projects. The Auto-BIMLearn would leverage on historical data, tacit knowledge(lesson-learnt) and asset management-information to support design, construction and asset management decisions."

“BIM被吹捧为解决影响建筑业生产力问题的有效方法。BIM工作组假设，随着BIM的采用，“通过使用开放共享的资产信息，可以实现成本，价值和碳性能的显着改善”。为了强化其效益，政府的“建设2025“将BIM列为实现其目标的关键要素，即降低33%的成本，加快50%的交付，减少50%的排放和改善50%的出口。尽管BIM的采用率有所增加，但企业仍然难以实施“真实的”BIM并实现预期效益。这是因为符合PAS-1192的命名惯例，以及需要足够的建筑信息，以在协作环境中伴随建筑材料/元件/产品的3D表示。对于已经超越BIM采用障碍的组织来说，主要的挑战仍然是让每个参与协作项目的人都使用CDE，并确定资产的不同方面和类型所需的确切（和特定）信息。因此，一些声称使用BIM的项目只收集了数字信息，而没有提供短期内有用的施工信息和长期的资产管理数据。尽管存在这些挑战，目前还没有工具来支持组织采用BIM并遵守标准，利用以前的项目经验教训/历史数据，支持自动化施工-运营-建筑-信息-交换（COBie），并促进与产品制造商的供应链集成。在此基础上，该项目采用机器学习（ML）和大数据分析技术，创建了一个创新工具（Auto-BIM）作为BIM工具的插件。它由以下四个要素组成：1.** CDE方法中BIM模型的自动化命名（Auto-BIMName）--** 这有助于项目团队按照PAS-1192和BS-EN-ISO-19650的一致性/合规性命名其文件。它还将有助于自动映射标题栏，目前正在合作公司/发起人和角色之间手动完成。2\.**建筑信息自动填充（Auto-BIM填充）-** 这将使用相关元数据（包括Omniclass分类、型号、服务信息、材料等）预填充产品/元素的3D表示。这将促进项目沟通/协作的传统方法，并加速BIM采用和效益实现。** BIM对象和模型数据自动共享 *（Auto-BIMShare）-** Auto-BIMShare提供了一个独特的平台，用于共享可重用对象库和相关信息，以促进跨软件边界的通用语言。它还将为制造商提供机会，使他们的产品/材料可供潜在的规格和买家。Auto-BIMShare将促进项目内/跨项目的设计、采购和维护/运营团队之间的信息共同创建/共享4\。**自动BIM学习平台（Auto-BIMLearn）-**BIM目前没有诊断项目的能力。Auto-BIMLearn将利用历史数据、隐性知识（经验教训）和资产管理信息来支持设计、施工和资产管理决策。"