Public Funding for Innovation Fire Engineering Limited

The review into the unfortunate Grenfell fire disaster uncovered sharp practices in building regulation regime, which is described as shocking in the 21st century United Kingdom. To tackle the challenges and ensure that everyone does the right thing and those who try to cut corner are held accountable. In line with this, Dame Judith Hackett led team suggested the needs for a new intelligent system of regulation and enforcement for high-rise and complex buildings. _Such system is expected to facilitate a high level of automation to evaluate and diagnose compliance with the array of fire safety legislation such as the_ Approved Document B, HTM 05-02, BS 9999, BS 9991 and BB100, some of which has gone through review processes since the aftermath of Grenfell fire enquiry. To support the move towards automated fire safety compliance diagnostic, our consortium has recently completed Fire-BIM project, which evaluates building design, identify areas of non-conformances, and suggest design changes for meeting fire safety requirements. The collaboration among Innovation Fire Engineering, P+HS Architects, Anglestack Limited, Leeds Beckett University, Farrans Construction PLC, CLM consultancy and University of Hertfordshire has led to the development of FireBIM platform as an automated portal for fire safety compliance diagnostic, which is currently being exploited by a spin out company. However, our FireBIM system is a rule-based design, involving manual extraction of fire safety rules that is coded into MongoDB as a NoSQL database. Thus, this feasibility study provides opportunity for automatically capturing building safety rule using Natural Language Processing (NLP) as a technique in Artificial Intelligence. The feasibility study will be used to determine the optimal approach for executing automated rule capturing and its implementation as a decision support system, covering text pre-processing, sentence segmentation, named entity recognition (in line with building components and elements), rule identification through pattern matching or rule-based parsing, rule extraction and entity capturing, rule classification and categorization, and rule quality assurance through comparison with existing manually extracted rule database. The result of the feasibility study would support in automating rule generation for our FireBIM diagnostics platform, thereby culminating in AI-FireBIM tool. This would evaluate building design for compliance with Approved Document B 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)Suggesting required design changes and fire safety standard requirements for different elements.

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.