Machine Learning-Enabled Fire Performance Diagnostics BIM Solution (FireBIM) for Building Design and Construction Compliance Check

用于建筑设计和施工合规性检查的机器学习消防性能诊断 BIM 解决方案 (FireBIM)

• 批准号: 10002279
• 金额: $ 48.34万
• 依托单位: INNOVATION FIRE ENGINEERING LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10002279
• 关键词: Machine; Learning; Enabled; Fire; Performance

The unfortunate Grenfell fire disaster, where 72 lives were lost, has gone down as one of the UK's worst disaster of modern time. As a way of preventing future occurrence of such tragedy, one of the key recommendations of government's commissioned report (Hackett,2018) suggests the need for intelligent systems of regulation and enforcement to facilitate compliance with arrays of regulations and standards, including Approved Document B, HTM05-02, BS9999, BS9991 and BB100, among others. This is also reinforced by several studies (e.g. Noren et al.,2018; Sun,2020) which suggested integration of fire performance diagnostics with Building Information Modelling (BIM) as optimal approach for mitigating fire disasters. However, development in such areas is hindered, as regulations and standards are written in natural languages, only comprehensible by domain experts, and not usually for machines to process. This implies that compliance with vital safety standards is prone to error, subjectivity, corner-cutting, and additional time in correcting non-conformances identified through manual processes.Advances in the field of machine learning and analytics offers opportunities for automating building safety requirement compliance check, which the proposed project seeks to implement. The project aims to create an intelligent innovative system (FireBIM), as BIM-based solution, consisting of two key elements as follows:Platform 1--Design Diagnostics Platform for Fire Performance Compliance (BIMFire-Diagnostics): This platform evaluates building design for compliance with fire safety standards and regulation, including Approved Document B, HTM 05-02, BS 9999, BS 9991 and BB100, by:(i)Automatically diagnosing proposed/as-built designs for compliance with provisions of targeted regulations/standards.(ii)Identifying areas of non-conformances with regulations and standards.(iii)Highlighting uncertified/unwarranted/uninsured areas, requiring detailing for approval.(iv)Suggesting improved and optimal design solutions.Platform 2--Materials and Components Decision Support for Fire Performance (BIMFire-DSS): Based on the requirements of building regulations and standards, up-to-date information from the industry and suppliers' information, this platform will provide decision supports for designers, contractors and building inspectors by:(i)Highlighting fire rating requirements for building elements.(ii)Digitising plethora of valid solutions for attaining compliance.(iii)Suggesting specific materials/detailing/specification for meeting performance standards.(iv)Flagging up materials/components/design solutions when unsafe for purpose.(v)Developing database of tested and certified materials/components.The project employs machine learning to train intelligent model that will identify and predict building regulations infringements/non-conformances in building designs. Text mining will be implemented to translate standards/regulation documents into machine-understandable formats, with automated reasoning and inference mechanism applied to the machine processable information for automated compliance diagnostics.

不幸的格伦费尔火灾造成72人丧生，已成为英国现代最严重的灾难之一。作为防止未来发生此类悲剧的一种方式，政府委托报告（Hackett，2018）的主要建议之一是需要智能监管和执法系统，以促进遵守一系列法规和标准，包括批准文件B，HTM 05 -02，BS 9999，BS 9991和BB 100等。这一点也得到了几项研究的加强（例如Noren等人，二〇一八年; Sun，2020年），建议将火灾性能诊断与建筑信息建模（BIM）相结合，作为减轻火灾的最佳方法。然而，这些领域的发展受到了阻碍，因为法规和标准是用自然语言编写的，只有领域专家才能理解，而机器通常无法处理。这意味着遵守重要的安全标准很容易出现错误，主观性，偷工减料，以及纠正通过手动过程发现的不符合要求的额外时间。机器学习和分析领域的进步为自动化建筑物安全要求合规性检查提供了机会，拟议项目寻求实施。该项目旨在创建一个智能创新系统（FireBIM），作为基于BIM的解决方案，由以下两个关键要素组成：平台1-消防性能合规性设计诊断平台（BIMFire-Diagnostics）：该平台通过以下方式评估建筑设计是否符合消防安全标准和法规，包括批准文件B、HTM 05-02、BS 9999、BS 9991和BB 100：（i）自动诊断拟议/竣工设计是否符合目标法规/标准的规定。㈡查明不符合条例和标准的领域。㈢突出未核证/无保证/无保险的地区，要求详细说明以供核准。平台2-材料和部件防火性能决策支持：根据建筑法规和标准的要求、来自行业的最新信息和供应商的信息，该平台将通过以下方式为设计师、承包商和建筑检查员提供决策支持：（二）将大量实现合规的有效解决方案数字化。（iii）建议符合性能标准的具体材料/细节/规格。（iv）在材料/部件/设计解决方案不安全时进行标记。㈤开发经测试和认证的材料/部件数据库：该项目采用机器学习来训练智能模型，以查明和预测建筑设计中违反建筑法规/不符合规定的情况。将实施文本挖掘，将标准/法规文件翻译成机器可理解的格式，并将自动推理和推理机制应用于机器可处理的信息，以进行自动合规性诊断。